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1
An, Ning, Shi-Ying Sun, Xiao-Guang Zhao, and Zeng-Guang Hou. "Remember like humans." International Journal of Advanced Robotic Systems 14, no. 1 (2017): 172988141769231. http://dx.doi.org/10.1177/1729881417692313.
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Visual tracking is a challenging computer vision task due to the significant observation changes of the target. By contrast, the tracking task is relatively easy for humans. In this article, we propose a tracker inspired by the cognitive psychological memory mechanism, which decomposes the tracking task into sensory memory register, short-term memory tracker, and long-term memory tracker like humans. The sensory memory register captures information with three-dimensional perception; the short-term memory tracker builds the highly plastic observation model via memory rehearsal; the long-term memory tracker builds the highly stable observation model via memory encoding and retrieval. With the cooperative models, the tracker can easily handle various tracking scenarios. In addition, an appearance-shape learning method is proposed to update the two-dimensional appearance model and three-dimensional shape model appropriately. Extensive experimental results on a large-scale benchmark data set demonstrate that the proposed method outperforms the state-of-the-art two-dimensional and three-dimensional trackers in terms of efficiency, accuracy, and robustness.
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Zhang, Jianming, Yifei Liang, Xiaoyi Huang, Li-Dan Kuang, and Bin Zheng. "Siamese Visual Tracking with Spatial-Channel Attention and Ranking Head Network." Electronics 12, no. 20 (2023): 4351. http://dx.doi.org/10.3390/electronics12204351.
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Trackers based on the Siamese network have received much attention in recent years, owing to its remarkable performance, and the task of object tracking is to predict the location of the target in current frame. However, during the tracking process, distractors with similar appearances affect the judgment of the tracker and lead to tracking failure. In order to solve this problem, we propose a Siamese visual tracker with spatial-channel attention and a ranking head network. Firstly, we propose a Spatial Channel Attention Module, which fuses the features of the template and the search region by capturing both the spatial and the channel information simultaneously, allowing the tracker to recognize the target to be tracked from the background. Secondly, we design a ranking head network. By introducing joint ranking loss terms including classification ranking loss and confidence&IoU ranking loss, classification and regression branches are linked to refine the tracking results. Through the mutual guidance between the classification confidence score and IoU, a better positioning regression box is selected to improve the performance of the tracker. To better demonstrate that our proposed method is effective, we test the proposed tracker on the OTB100, VOT2016, VOT2018, UAV123, and GOT-10k testing datasets. On OTB100, the precision and success rate of our tracker are 0.925 and 0.700, respectively. Considering accuracy and speed, our method, overall, achieves state-of-the-art performance.
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Kim, A.-Yeong, Hyun-Je Song, and Seong-Bae Park. "A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing." Computational Intelligence and Neuroscience 2018 (October 18, 2018): 1–11. http://dx.doi.org/10.1155/2018/5798684.
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Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the requirements of the user, accurate state tracking is important for spoken dialog systems. This paper proposes a two-step neural dialog state tracker which is composed of an informativeness classifier and a neural tracker. The informativeness classifier which is implemented by a CNN first filters out noninformative utterances in a dialog. Then, the neural tracker estimates dialog states from the remaining informative utterances. The tracker adopts the attention mechanism and the hierarchical softmax for its performance and fast training. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs with the standard DSTC4 data set. Our experimental results prove the effectiveness of the proposed model by showing that the proposed model outperforms the neural trackers without the informativeness classifier, the attention mechanism, or the hierarchical softmax.
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Kuttybay, Nurzhigit, Ahmet Saymbetov, Saad Mekhilef, et al. "Optimized Single-Axis Schedule Solar Tracker in Different Weather Conditions." Energies 13, no. 19 (2020): 5226. http://dx.doi.org/10.3390/en13195226.
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Improving the efficiency of solar panels is the main task of solar energy generation. One of the methods is a solar tracking system. One of the most important parameters of tracking systems is a precise orientation to the Sun. In this paper, the performance of single-axis solar trackers based on schedule and light dependent resistor (LDR) photosensors, as well as a stationary photovoltaic installation in various weather conditions, were compared. A comparative analysis of the operation of a manufactured schedule solar tracker and an LDR solar tracker in different weather conditions was performed; in addition, a simple method for determining the rotation angle of a solar tracker based on the encoder was proposed. Finally, the performance of the manufactured solar trackers was calculated, taking into account various weather conditions for one year. The proposed single-axis solar tracker based on schedule showed better results in cloudy and rainy weather conditions. The obtained results can be used for designing solar trackers in areas with a variable climate.
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Zhang, Ximing, Mingang Wang, and Lin Cao. "Real-Time Thermal Infrared Tracking Based on Collaborative Online and Offline Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 6 (2018): 1052–58. http://dx.doi.org/10.1051/jnwpu/20183661052.
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Most tracking-by-detection based trackers employ the online model update scheme based on the spatiotemporal consistency of visual cues. In presence of self-deformation, abrupt motion and heavy occlusion, these trackers suffer from different attributes and are prone to drifting. The model based on offline training, namely Siamese networks is invariant when suffering from the attributes. While the tracking speed of the offline method can be slow which is not enough for real-time tracking. In this paper, a novel collaborative tracker which decomposes the tracking task into online and offline modes is proposed. Our tracker switches between the online and offline modes automatically based on the tracker status inferred from the present failure tracking detection method which is based on the dispersal measure of the response map. The present Real-Time Thermal Infrared Collaborative Online and Offline Tracker (TCOOT) achieves state-of-the-art tracking performance while maintaining real-time speed at the same time. Experiments are carried out on the VOT-TIR-2015 benchmark dataset and our tracker achieves superior performance against Staple and Siam FC trackers by 3.3% and 3.6% on precision criterion and 3.8% and 5% on success criterion, respectively. The present method is real-time tracker as well.
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Li, Chunbao, and Bo Yang. "Robust Scale Adaptive Visual Tracking with Correlation Filters." Applied Sciences 8, no. 11 (2018): 2037. http://dx.doi.org/10.3390/app8112037.
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Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.
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Fiaz, Mustansar, Arif Mahmood, Ki Yeol Baek, Sehar Shahzad Farooq, and Soon Ki Jung. "Improving Object Tracking by Added Noise and Channel Attention." Sensors 20, no. 13 (2020): 3780. http://dx.doi.org/10.3390/s20133780.
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CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.
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Zalesky, B. A., V. A. Ivanyukovich, K. V. Reer, and D. A. Starikovich. "Comparative analysis of object tracking algorithms." Informatics 22, no. 1 (2025): 66–72. https://doi.org/10.37661/1816-0301-2025-22-1-66-72.
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Objectives. The article presents the results of calculation and comparative analysis of the characteristics of the algorithm proposed by the authors in [1] for tracking an object captured by a video camera, when solving the urgent task of automatic detection and tracking of drones. Two algorithms were selected for comparative analysis, one of which is the currently known open source ByteTrack tracker, and the other is a simple tracker based on the use of the neural network, correlation comparison together with Kalman filter. The first tracker was chosen because it can be implemented in C++ without using third-party libraries and frameworks and used on small computers in real time. The second tracker was used to determine how much better new trackers are than simple, long-used ones. The specificity of the used algorithms is automatic detection and capture of the drone, its further reliable tracking, quick repeated capture in case of tracking failure, capture of another drone when the tracked object disappears. In the used trackers, drone detection in video frames is carried out using a neural network detector, and tracking is done with the help of the neural network detector and developed tracking algorithms.Methods. To perform a comparative analysis of object tracking algorithms, two datasets consisting of video frames that contain drone images were created and labeled. The training dataset consists of 36895 frames whereas testing one contains 8678 images. The videos of the training and test datasets were shot with different cameras in different conditions. To train the neural network part of the trackers, versions of the algorithms were written in the Python programming language, and to calculate and analyze characteristics in conditions close to real ones, in C++, which required converting the trained network using the TensorRT framework. Software tools for gathering and processing experimental data were also implemented.Results. The comparative analysis of three object tracking algorithms allowed us to calculate and compare the characteristics of these trackers, as well as draw conclusions about the method of training the used neural network detector; about the possibility of using trackers in real time on budget personal computers with budget video cards that have the CUDA software and hardware architecture, about the applicability of two of them for solving the problem of practical tracking of drones observed by video cameras with sufficient accuracy and reliability. Of the three tested algorithms the tracker previously developed by the authors has the best characteristics.Conclusion. The comparative analysis of the above-mentioned trackers showed the possibility of practical application of the tracker and the ByteTrack algorithm for solving the problem of tracking drones, however, there is still a problem with detecting small-sized unmanned aerial vehicles.
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Dahire, Aniket, Tejas Dhamale, Shubham Dhanke, Sameer Gaikwad, and Ms Roshani Parate. "Team Tracker: A Project Management Tool." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem38265.
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This paper introduces "Team Tracker," a project management tool developed using Asana and Next.js. The tool integrates key functionalities such as audio and video calling, real-time task tracking, and an embedded browser, creating an all-in-one solution for remote team collaboration. The objective of Team Tracker is to streamline workflow by eliminating the need for multiple platforms, thus improving team efficiency and communication. A comparative analysis with existing tools like Jira and Slack demonstrates how Team Tracker addresses the gaps in current project management solutions by offering enhanced real-time communication and task management capabilities. The system's architecture, challenges encountered during development, expected outcomes, and potential future enhancements are also discussed. Team Tracker is poised to provide a comprehensive, scalable solution for modern project management needs. Keywords: project management, Asana, Next.js, real-time communication, task tracking, Team Tracker.
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Wu, Han, Jiahao Nie, Zhiwei He, Ziming Zhu, and Mingyu Gao. "One-Shot Multiple Object Tracking in UAV Videos Using Task-Specific Fine-Grained Features." Remote Sensing 14, no. 16 (2022): 3853. http://dx.doi.org/10.3390/rs14163853.
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Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorporates detection and ReID in a unified network, has gained attention due to its fast inference speed. It significantly reduces the computational overhead by making two subtasks share features. However, most existing one-shot trackers struggle to achieve robust tracking in UAV videos. We observe that the essential difference between detection and ReID leads to an optimization contradiction within one-shot networks. To alleviate this contradiction, we propose a novel feature decoupling network (FDN) to convert shared features into detection-specific and ReID-specific representations. The FDN searches for characteristics and commonalities between the two tasks to synergize detection and ReID. In addition, existing one-shot trackers struggle to locate small targets in UAV videos. Therefore, we design a pyramid transformer encoder (PTE) to enrich the semantic information of the resulting detection-specific representations. By learning scale-aware fine-grained features, the PTE empowers our tracker to locate targets in UAV videos accurately. Extensive experiments on VisDrone2021 and UAVDT benchmarks demonstrate that our tracker achieves state-of-the-art tracking performance.
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Assoc.Prof, Ms Rajashree Sutrawe, Erra Abhinav, and Galakatla Thanusha. "TASK PULSE." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem39659.
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Effective workforce management is essential in today's fast-paced work environment. Task Pulse is a mobile-based solution designed to address common workforce management challenges. It offers real-time project and task tracking, a skill development tracker, and seamless integration with productivity tools. The app's features promote employee growth and align organizational objectives. Planned enhancements aim to further optimize workflows and foster a culture of continuous improvement. Keywords: Employee Management, Workforce Analytics, Task Tracking, Mobile Solutions, Skill Development
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Zhao, Long, Mubarak Adam Ishag Mahmoud, Honge Ren, and Meng Zhu. "A Visual Tracker Offering More Solutions." Sensors 20, no. 18 (2020): 5374. http://dx.doi.org/10.3390/s20185374.
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Most trackers focus solely on robustness and accuracy. Visual tracking, however, is a long-term problem with a high time limitation. A tracker that is robust, accurate, with long-term sustainability and real-time processing, is of high research value and practical significance. In this paper, we comprehensively consider these requirements in order to propose a new, state-of-the-art tracker with an excellent performance. EfficientNet-B0 is adopted for the first time via neural architecture search technology as the backbone network for the tracking task. This improves the network feature extraction ability and significantly reduces the number of parameters required for the tracker backbone network. In addition, maximal Distance Intersection-over-Union is set as the target estimation method, enhancing network stability and increasing the offline training convergence rate. Channel and spatial dual attention mechanisms are employed in the target classification module to improve the discrimination of the trackers. Furthermore, the conjugate gradient optimization strategy increases the speed of the online learning target classification module. A two-stage search method combined with a screening module is proposed to enable the tracker to cope with sudden target movement and reappearance following a brief disappearance. Our proposed method has an obvious speed advantage compared with pure global searching and achieves an optimal performance on OTB2015, VOT2016, VOT2018-LT, UAV-123 and LaSOT while running at over 50 FPS.
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Zhu, Xiaoning, Yannan Jia, Sun Jian, Lize Gu, and Zhang Pu. "ViTT: Vision Transformer Tracker." Sensors 21, no. 16 (2021): 5608. http://dx.doi.org/10.3390/s21165608.
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This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.
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Fan, Baojie, Xiaomao Li, Yang Cong, and Yandong Tang. "Structured and weighted multi-task low rank tracker." Pattern Recognition 81 (September 2018): 528–44. http://dx.doi.org/10.1016/j.patcog.2018.04.002.
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He, Haiyu, Zhen Chen, Zhen Li, Xiangdong Liu, and Haikuo Liu. "Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity." Sensors 23, no. 17 (2023): 7516. http://dx.doi.org/10.3390/s23177516.
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Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model’s dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target’s scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.
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Manners, J., H. Scott, A. Guyett, N. Stuart, P. Catcheside, and E. Kemps. "O058 Sleep estimates from an under-mattress sensor can predict vigilance, working memory, and mental arithmetic performance." Sleep Advances 5, Supplement_1 (2024): A21. https://doi.org/10.1093/sleepadvances/zpae070.058.
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Abstract Introduction Sleep is vitally important to maintain cognitive function. Sleep trackers can reliably estimate several sleep metrics to help predict cognitive performance, but associations between sleep-tracker estimated sleep and cognitive domains are unclear. This study examined associations between sleep tracker-estimated sleep and next-day cognitive performance across several domains, including vigilant attention, working memory, and risky decision-making. Methods Twenty four participants (mean[SD] age=28[9] years, 12/12 male/female) attended the sleep laboratory twice for 8-day simulated shift-work experimental protocols under two lighting conditions (dim, blue-depleted vs. blue-enriched circadian-informed lighting). Following a baseline sleep, participants remained awake for 27h and transitioned to sleeping between 10:00-19:00 and undertaking cognitive tasks from 00:00-08:00 for four days. Cognitive tasks included the Digit-Symbol Substitution task (DSST), Continuous Performance task, Iowa Gambling task, Operation Span task, Psychomotor Vigilance test (PVT), and Stroop task. Sleep was assessed using an under-mattress sensor (Withings Sleep Analyzer). Mixed effects and random forest models tested associations between estimated sleep and cognitive performance. Results Significant associations were found between sleep metrics and PVT reaction time (R²=.13, p=.008), Operation Span arithmetic errors (R²=.15, p=.023), proportion correct on DSST and Stroop tasks, (R²=.06, p=.045; R²=.09, p=.003), and Stroop reaction time (R²=.19, p=.004). Random forest models demonstrated strong associations between cognitive performance and estimates of sleep architecture, snoring, and cardiovascular estimates. Conclusion Sleep trackers can help predict next-day cognitive performance, particularly in vigilance, working memory, and mental arithmetic. This may be a practical strategy to help manage sleep-related cognitive impairments and enhance worker safety in shift work conditions.
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Sharaeva, Regina Airatovna, and Vlada Vladimirovna Kugurakova. "Assessment of time reduction when using a modified task-tracking methodology in IT project management." Program Systems: Theory and Applications 13, no. 3 (2022): 307–24. http://dx.doi.org/10.25209/2079-3316-2022-13-3-307-324.
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Task-trackers that allow automating management tasks are traditionally used for IT project development management. Popular tools were analyzed, and new requirements were formulated for task and project management systems in general for any highly specialized areas of IT development. The author’s methodology for task-tracking systems, not found in any of the considered solutions, was developed. Practical implementation of the proposed approach showed that it is possible to solve management problems much more efficiently: optimization reaches more than 50% in some cases. In addition, the developed tool ProjectAR allows leveling several risks. Comparison with the popular task tracker Asana, which is the closest to ProjectAR by its functionality, was conducted to prove the hypothesis of time reduction for management tasks. In addition to the time metric, the risk of incorrect integration of generated development artifacts was selected as a criterion for tool comparison. The tools were compared based on the number of templates needed to implement IT solutions and the number of typical projects. At the end, a vision for tool development is given.
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Zhu, Chengfei, Shan Jiang, Shuxiao Li, and Xiaosong Lan. "Efficient and Practical Correlation Filter Tracking." Sensors 21, no. 3 (2021): 790. http://dx.doi.org/10.3390/s21030790.
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Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).
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Vardhan, S. Harsha, M. Sohan, M. Satya Abhiram, and D. Lakshmi Rohitha. "CRYPTO PORTFOLIO TRACKER AND ALERT APPLICATION." International Journal Of Trendy Research In Engineering And Technology 06, no. 05 (2022): 33–39. http://dx.doi.org/10.54473/ijtret.2022.6506.
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The objective of the paper is to build a platform for cryptocurrency investors, who purchase cryptocurrencies on different cryptocurrency exchange platforms where the investor can create his portfolio on the platform and track the real-time profit/loss of his total portfolio balance as well as in each cryptocurrency he has invested in and can also set reminders. With this platform, he can also link his WhatsApp to get notification alerts of his portfolio balance and profit/loss in real-time. This real-time notification alert feature is implemented with Django Celery and Redis. Celery is a task queue with a focus on real-time processing, which also supports task scheduling. Redis is a message broker.
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Vihlman, Mikko, and Arto Visala. "Optical Flow in Deep Visual Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12112–19. http://dx.doi.org/10.1609/aaai.v34i07.6890.
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Single-target tracking of generic objects is a difficult task since a trained tracker is given information present only in the first frame of a video. In recent years, increasingly many trackers have been based on deep neural networks that learn generic features relevant for tracking. This paper argues that deep architectures are often fit to learn implicit representations of optical flow. Optical flow is intuitively useful for tracking, but most deep trackers must learn it implicitly. This paper is among the first to study the role of optical flow in deep visual tracking. The architecture of a typical tracker is modified to reveal the presence of implicit representations of optical flow and to assess the effect of using the flow information more explicitly. The results show that the considered network learns implicitly an effective representation of optical flow. The implicit representation can be replaced by an explicit flow input without a notable effect on performance. Using the implicit and explicit representations at the same time does not improve tracking accuracy. The explicit flow input could allow constructing lighter networks for tracking.
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Chen, Jia, Fan Wang, Yingjie Zhang, Yibo Ai, and Weidong Zhang. "SiamMFC: Visual Object Tracking Based on Mainfold Full Convolution Siamese Network." Sensors 21, no. 19 (2021): 6388. http://dx.doi.org/10.3390/s21196388.
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Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance.
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Jiang, Ming-xin, Chao Deng, Ming-min Zhang, Jing-song Shan, and Haiyan Zhang. "Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking." Complexity 2018 (November 28, 2018): 1–8. http://dx.doi.org/10.1155/2018/5676095.
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Visual tracking is still a challenging task due to occlusion, appearance changes, complex motion, etc. We propose a novel RGB-D tracker based on multimodal deep feature fusion (MMDFF) in this paper. MMDFF model consists of four deep Convolutional Neural Networks (CNNs): Motion-specific CNN, RGB- specific CNN, Depth-specific CNN, and RGB-Depth correlated CNN. The depth image is encoded into three channels which are sent into depth-specific CNN to extract deep depth features. The optical flow image is calculated for every frame and then is fed to motion-specific CNN to learn deep motion features. Deep RGB, depth, and motion information can be effectively fused at multiple layers via MMDFF model. Finally, multimodal fusion deep features are sent into the C-COT tracker to obtain the tracking result. For evaluation, experiments are conducted on two recent large-scale RGB-D datasets and results demonstrate that our proposed RGB-D tracking method achieves better performance than other state-of-art RGB-D trackers.
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Lin, Hui-Ju, Li-Wei Chou, Kang-Ming Chang, Jing-Fong Wang, Sih-Huei Chen, and Rimuljo Hendradi. "Visual Fatigue Estimation by Eye Tracker with Regression Analysis." Journal of Sensors 2022 (January 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/7642777.
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The traditional way to detect visual fatigue is to use the questionnaire or to use critical fusion frequency of high-frequency exchanges due to eye fatigue. The objective of this study was to explore whether eye movement behavior can be used as an objective tool to detect visual fatigue. Thirty-three participants were tested in this study. Their subjective visual fatigue survey, critical fusion frequency, and eye tracker of one minute gaze were measured before and after 20 minutes visual fatigue task. There were significant differences before and after visual fatigue task on survey and eye tracker-derived features. By multiple regression analysis with four eye tracker features, total fixation time duration of the inner circle, longest continuous duration of inner circle viewing time, maximum saccade distance, and focus radius, the regression R square value was greater than 0.9 for all critical fusion frequency data and when subjective visual fatigue assessment was greater than 12 points. In conclusion, eye movement behavior can be used to detect visual fatigue more sensitively even than the traditional critical flicker fusion assessment. Eye tracker can also provide well regression model to fit traditional critical fusion frequency measurement and subjective visual fatigue survey.
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Raut, Janhavi Ashok. "Stakeholder Analysis and Task Tracker System for PMAY Project Management." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 126–33. https://doi.org/10.22214/ijraset.2025.70116.
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Abstract: This paper presents a field-validated study aimed at improving the project management framework under the PradhanMantriAwasYojana(PMAY).Throughin-depthstakeholdersurveys,on-groundchallengesweremapped,leadingto the developmentofacustomdigitalprototype—aninteractiveTaskTrackerSystem.Theprototypeaddressescriticalgapsin stakeholder communication, transparency, multilingual accessibility, and deadline adherence. The proposed tool includes stakeholderspecific dashboards, multilingual interfaces, visual progress indicators, and an alert mechanism. The results demonstrate the system's potential to reduce delays, streamline workflows, and enhance accountability. Recommendations and future integration paths are also outlined
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Journal, IJSREM. "HEALTH TRACKER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29095.
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The abstract of the health tracker mainly discusses about the health safety and precaution method to protect from health issues The usage of this mobile application can make it easier to overlook the importance of eating a healthy diet. mobile app offer an equitable infrastructure that may be used to offer high-quality, reasonably priced tools for behaviour control and monitoring. This mobile application's design enables the user to retrieve nutrition information whenever needed, customise and personalise it, and keep a close eye on their eating patterns. This Android app provides a one-stop shop for all questions or problems pertaining to health. It performs a number of tasks, including diet monitoring, food nutrition analysis, BMI calculation, and details on several common medications. Nutritionspy is a cutting-edge mobile application designed to empower users in achieving their health and wellness goals through efficient and personalized nutrition tracking. In the fast-paced world of today, eating a healthy, balanced diet is essential for general wellbeing. it offers a user-friendly interface coupled with advanced features to simplify the process of monitoring and managing dietary habits. This app is a comprehensive nutrition tracking app that not only simplifies the process of monitoring dietary intake but also empowers users to make informed decisions about their nutrition. By leveraging advanced technology, personalized insights, and user- friendly features, it is positioned to become an essential tool for individuals committed to achieving and maintaining a healthy lifestyle. here are growing numbers of nutrition apps accessible for smartphones and other mobile devices. They can assist in lightening the laborious task of recording consumption for self- monitoring and nutritional assessment. This makes it possible for people to regulate how much food they eat, encourage physical activity, and support leading healthy lives. Research on systematic analysis mapping studies in this field is still lacking, nevertheless. Finding implementation solutions for nutritional self-monitoring using a mobile application is the aim of this project. Seven groups of recurring themes emerged from the study on mobile applications for nutritional self- monitoring: body mass index, techniques, nutrition algorithms, noncommunicable diseases, metrics for disease detection, and attitude towards improved dietary behaviours. enables real-time meal recording, the convenience of automatically calculating the calorie content of foods consumed, and the potential to improve the delivery of health behaviour modification interventions to large populations is the focus of current research trends regarding dietary self-monitoring. Keywords: Healthcare, Nutrition tracker, Progress check, Calorie calculator
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Asr, Fatemeh Torabi, Mohammad Mazraeh, Alexandre Lopes, et al. "The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media." PLOS ONE 16, no. 1 (2021): e0245533. http://dx.doi.org/10.1371/journal.pone.0245533.
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We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily publication of seven English-language Canadian news outlets and enhances the data with multiple layers of linguistic information. We describe the Natural Language Processing technology behind this system, the curation of off-the-shelf tools and resources that we used to build it, and the parts that we developed. We evaluate the system in each language processing task and report errors using real-world examples. Finally, by applying the Tracker to the data, we provide valuable insights about the proportion of people mentioned and quoted, by gender, news organization, and author gender. Data collected between October 1, 2018 and September 30, 2020 shows that, in general, men are quoted about three times as frequently as women. While this proportion varies across news outlets and time intervals, the general pattern is consistent. We believe that, in a world with about 50% women, this should not be the case. Although journalists naturally need to quote newsmakers who are men, they also have a certain amount of control over who they approach as sources. The Gender Gap Tracker relies on the same principles as fitness or goal-setting trackers: By quantifying and measuring regular progress, we hope to motivate news organizations to provide a more diverse set of voices in their reporting.
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Narayana, Asha, and Narasimhadhan Venkata. "Enhanced Median Flow Tracker Based on Photometric Correction for Videos with Abrupt Changing Illumination." International Arab Journal of Information Technology 17, no. 2 (2019): 264–71. http://dx.doi.org/10.34028/iajit/17/2/15.
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Object tracking is a fundamental task in video surveillance, human-computer interaction and activity analysis. One of the common challenges in visual object tracking is illumination variation. A large number of methods for tracking have been proposed over the recent years, and median flow tracker is one of them which can handle various challenges. Median flow tracker is designed to track an object using Lucas-Kanade optical flow method which is sensitive to illumination variation, hence fails when sudden illumination changes occur between the frames. In this paper, we propose an enhanced median flow tracker to achieve an illumination invariance to abruptly varying lighting conditions. In this approach, illumination variation is compensated by modifying the Discrete Cosine Transform (DCT) coefficients of an image in the logarithmic domain. The illumination variations are mainly reflected in the low-frequency coefficients of an image. Therefore, a fixed number of DCT coefficients are ignored. Moreover, the Discrete Cosine (DC) coefficient is maintained almost constant all through the video based on entropy difference to minimize the sudden variations of lighting impacts. In addition, each video frame is enhanced by employing pixel transformation technique that improves the contrast of dull images based on probability distribution of pixels. The proposed scheme can effectively handle the gradual and abrupt changes in the illumination of the object. The experiments are conducted on fast-changing illumination videos, and results show that the proposed method improves median flow tracker with outperforming accuracy compared to the state-of-the-art trackers
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Dolatabadi, Marzieh, Jos Elfring, and René van de Molengraft. "Multiple-Joint Pedestrian Tracking Using Periodic Models." Sensors 20, no. 23 (2020): 6917. http://dx.doi.org/10.3390/s20236917.
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Estimating accurate positions of multiple pedestrians is a critical task in robotics and autonomous cars. We propose a tracker based on typical human motion patterns to track multiple pedestrians. This paper assumes that the legs’ reflection and extension angles are approximately changing periodically during human motion. A Fourier series is fitted in order to describe the moving, such as describing the position and velocity of the hip, knee, and ankle. Our tracker receives the position of the ankle, knee, and hip as measurements. As a proof of concept, we compare our tracker with state-of-the-art methods. The proposed models have been validated by experimental data, the Human Gait Database (HuGaDB), and the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) tracking benchmark. The results indicate that our tracker is able to estimate the reflection and extension angles with a precision of 90.97%. Moreover, the comparison shows that the tracking precision increases up to 1.3% with the proposed tracker when compared to a constant velocity based tracker.
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BING, Xinyang, Xiaofeng MAO, Liying ZHENG, Yubo ZHANG, and Zhongxiao LI. "Two-level cascade model for tracking pedestrians using thermal infrared video information." Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science 24, no. 3 (2023): 255–65. http://dx.doi.org/10.59277/pra-ser.a.24.3.07.
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Thermal infrared pedestrian tracking is a challenging task due to factors such as energy attenuation, sensor noise, occlusion, and complex backgrounds. In this paper, we design a two-level cascade model that tracks pedestrians in a thermal infrared video by the coarse-to-fine strategy to improve the tracking accuracy and success rate. The base tracker in the first level of our model is initialized and fine-tuned to get the first representation of a target which is then used to locate the target roughly. Aiming at finely locating a target, the second level consists of modality-specific part correlation filters that can capture patterns of thermal infrared pedestrians. The outputs of part correlation filters are aggregated together by normalized joint confidence that can effectively suppress low confidence predictions to make a final decision. We adaptively update each part filter by a weighted learning rate and accurately estimate pedestrian scale by a scale filter to improve tracking performance. The experimental results on the PTB-TIR benchmark show that the proposed cascade tracker further emphasizes crucial thermal infrared features. Thus it can effectively relieve the problem of object occlusion. Our experimental results show the superiority of the proposed tracker over the state-of-the-art trackers, including SRDCF, GFS-DCF, MCFTS, HDT, HCF, MLSSNet, HSSNet, SiamFC_tir, SVM, and L1APG.
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Zhiyong, An, Guan Hao, and Li Jinjiang. "Robust Visual Tracking Using the Bidirectional Scale Estimation." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3276103.
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Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.
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Bin Sun. "TransFiner: A full-scale refinement approach for multiple object tracking." ITU Journal on Future and Evolving Technologies 4, no. 4 (2023): 580–89. http://dx.doi.org/10.52953/clno2409.
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Multiple Object Tracking (MOT) is a task for containing detection and association. Plenty of trackers have achieved competitive performance. Unfortunately, for the lack of informative exchange on these subtasks, they are often biased toward one of the two and underperform in complex scenarios, such as the inevitable misses and mistaken trajectories of targets, when tracking individuals within a crowd. This paper proposes TransFiner, a transformer-based approach to post-refining MOT. It is a generic attachment framework that depends on query pairs, the bridge between an original tracker and TransFiner. Each query pair, through the fusion decoder, produces refined detection and motion clues for a specific object. Before that, they are feature-aligned and group-labeled under the guidance of tracking results (locations and class predictions) from the original tracker, finishing tracking refinement with focus and comprehensively. Experiments show that our design is effective, on the MOT17 benchmark, we elevate the CenterTrack from 67.8% MOTA and 64.7% IDF1 to 71.5% MOTA and 66.8% IDF1. The code is publicly available at https://github.com/BeenoSun/TransFiner.
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Li, Wanping, Jiajie Wu, Kuiying Yin, Guang Jiang, Chao Yu, and Lanyu Li. "A Method of Attention Analysis on Video." Journal of Physics: Conference Series 2253, no. 1 (2022): 012032. http://dx.doi.org/10.1088/1742-6596/2253/1/012032.
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Abstract Attention monitoring system is important for various tasks such as driving by alarming the person when he or she is not attending to the task at hand. Past research has not explored a usable attention monitoring system. In the current study, we used eye trackers, depth camera, and infrared cameras to assess the attention of the participants as they read texts. We extracted features from eye tracking and camera data, and then used convolutional neural network to predict the attention state of the participants. We found the eye tracker data yielded a 90% accuracy in predicting attentional state of the subjects. The camera data yielded over 70% accuracy in prediction.
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Tao, Yi, Fei Wang, Mohan Li, et al. "Improved siamese tracking for temporal data association." PLOS One 20, no. 4 (2025): e0320746. https://doi.org/10.1371/journal.pone.0320746.
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Temporal image data association is essential for visual object tracking tasks. This association task is typically stated as a process of connecting signals from the same object at different times along the time axis. Temporal data association is usually performed before state estimation. The accuracy of data association processing results is fundamental to guaranteeing the correctness of all subsequent procedures. This paper proposes an efficient approach for temporal data association focused on obtaining accurate data association processing results in Siamese network framework. Siamese network has recently achieved strong power in visual object tracking owing to its balanced accuracy and speed. Based on data association processing and multi-tracker collaboration, our algorithm achieves high accuracy and strong robustness, which outperforms several state-of-the-art trackers, including standard Siamese trackers.
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Brusinsky, N. A., A. A. Badarin, A. V. Andreev, V. M. Antipov, S. A. Kurkin, and A. E. Hramov. "Analysis of cognitive load in the Sternberg problem: eye-tracker study." Известия Российской академии наук. Серия физическая 87, no. 1 (2023): 125–28. http://dx.doi.org/10.31857/s0367676522700235.
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We studied physiological and behavioral characteristics of a person during prolonged solution of a cognitive task based on Sternberg paradigm. We found that evaluation of subjective fatigue and physiological characteristics such as blink duration and pupil size range of motion during the task solution are correlated to each other.
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Tiutiunnyk, Petro B., and Natalia A. Rybachok. "Analysis of Tasks Parameters of Solve the Problem of Determining Delays and Risks in Agile Projects." Control Systems and Computers, no. 2 (302) (2023): 61–66. http://dx.doi.org/10.15407/csc.2023.02.061.
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Agile methodology is actively used for project management. This article presents the results of determining which task parameters are important in determining delays and risks in Agile projects. The article provides information on the influence of parameters on the likelihood that a task is a risk or a delay. These parameters are typical for the Atlassian Jira bug tracker.
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de Boer, Pia S., Alexander J. A. M. van Deursen, and Thomas J. L. van Rompay. "Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers." JMIR Human Factors 7, no. 4 (2020): e22532. http://dx.doi.org/10.2196/22532.
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Background The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These IoT skills are expected to differ across the general population, thereby causing a new digital divide. Objective This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and traditional internet skills. Methods From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component—action plan construction. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component—action plan execution. Results The participants successfully completed 54% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56% (10.1/18) for action plan construction and 43% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills. Conclusions This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals.
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Manzoor, Sumaira, Ye-Chan An, Gun-Gyo In, Yueyuan Zhang, Sangmin Kim, and Tae-Yong Kuc. "SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model." Sensors 23, no. 10 (2023): 4906. http://dx.doi.org/10.3390/s23104906.
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Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second.
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Stalljann, Sarah, Lukas Wöhle, Jeroen Schäfer, and Marion Gebhard. "Performance Analysis of a Head and Eye Motion-Based Control Interface for Assistive Robots." Sensors 20, no. 24 (2020): 7162. http://dx.doi.org/10.3390/s20247162.
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Assistive robots support people with limited mobility in their everyday life activities and work. However, most of the assistive systems and technologies for supporting eating and drinking require a residual mobility in arms or hands. For people without residual mobility, different hands-free controls have been developed. For hands-free control, the combination of different modalities can lead to great advantages and improved control. The novelty of this work is a new concept to control a robot using a combination of head and eye motions. The control unit is a mobile, compact and low-cost multimodal sensor system. A Magnetic Angular Rate Gravity (MARG)-sensor is used to detect head motion and an eye tracker enables the system to capture the user’s gaze. To analyze the performance of the two modalities, an experimental evaluation with ten able-bodied subjects and one subject with tetraplegia was performed. To assess discrete control (event-based control), a button activation task was performed. To assess two-dimensional continuous cursor control, a Fitts’s Law task was performed. The usability study was related to a use-case scenario with a collaborative robot assisting a drinking action. The results of the able-bodied subjects show no significant difference between eye motions and head motions for the activation time of the buttons and the throughput, while, using the eye tracker in the Fitts’s Law task, the error rate was significantly higher. The subject with tetraplegia showed slightly better performance for button activation when using the eye tracker. In the use-case, all subjects were able to use the control unit successfully to support the drinking action. Due to the limited head motion of the subject with tetraplegia, button activation with the eye tracker was slightly faster than with the MARG-sensor. A further study with more subjects with tetraplegia is planned, in order to verify these results.
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Li, Xi, Ruixiang Zhu, Xianguo Yu, and Xiangke Wang. "High-Performance Detection-Based Tracker for Multiple Object Tracking in UAVs." Drones 7, no. 11 (2023): 681. http://dx.doi.org/10.3390/drones7110681.
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As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.
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BOSSE, TIBOR, ZULFIQAR A. MEMON, ROGIER OORBURG, JAN TREUR, MUHAMMAD UMAIR, and MICHAEL DE VOS. "A SOFTWARE ENVIRONMENT FOR AN ADAPTIVE HUMAN-AWARE SOFTWARE AGENT SUPPORTING ATTENTION-DEMANDING TASKS." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 819–46. http://dx.doi.org/10.1142/s0218213011000310.
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This paper presents a software environment providing human-aware ambient support for a human performing a task that demands substantial amounts of attention. The agent obtains human attention-awareness in an adaptive manner by use of a dynamical model of human attention, gaze sensoring by an eye-tracker, and information about features of the objects in the environment which is parameterised for characteristics of the human specified above. The agent uses a built-in adaptation model to adapt on the fly, the values of these parameters to the personal characteristics of the human. The software agent has been implemented in a component-based manner within the Adobe®Flex®environment, thereby also integrating the Tobii®eye-tracker. It has been applied in a setup for a task where the human has to identify enemies and allies, and eliminate the enemies.
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Jung, Ilchae, Kihyun You, Hyeonwoo Noh, Minsu Cho, and Bohyung Han. "Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11205–12. http://dx.doi.org/10.1609/aaai.v34i07.6779.
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We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-tune its model parameters in only a few gradient-descent iterations during tracking while pruning its network channels using the target ground-truth at the first frame. Such a learning problem is formulated as a meta-learning task, where a meta-tracker is trained by updating its meta-parameters for initial weights, learning rates, and pruning masks through carefully designed tracking simulations. The integrated meta-tracker greatly improves tracking performance by accelerating the convergence of online learning and reducing the cost of feature computation. Experimental evaluation on the standard datasets demonstrates its outstanding accuracy and speed compared to the state-of-the-art methods.
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Hu, Yangguang, Mingqing Xiao, Shaoyi Li, Yao Yang, and Sijie Wu. "Scale prediction based MDNet for infrared target tracking." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2711–23. http://dx.doi.org/10.3233/jifs-190787.
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Infrared target tracking is increasingly becoming important for various applications in recent years. However, it is still a challenging task as limited information can be obtained from the infrared image. Inspired by the excellent performance of deep tracker, a novel tracker based on MDNet is proposed. As the prior information has great value for target tracking, a modified Back-Propagation network is used for predicting the scale of target during tracking. The result of the prediction is used for generating candidate windows for online learning, which can improve the performance of tracker. To evaluate the proposed tracking algorithm, we performed experiments on the VOT-TIR2016 and AMCOM infrared data. The experimental results demonstrate that our algorithm provides a 1.94% relative gain in accuracy and 21.4% in robustness on VOT-TIR2016 when compared with MDNet.
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Yuan, Di, Xiaojun Chang, Zhihui Li, and Zhenyu He. "Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (2022): 1–18. http://dx.doi.org/10.1145/3486678.
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Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.
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Jones, Ethan, Winyu Chinthammit, Weidong Huang, Ulrich Engelke, and Christopher Lueg. "Symmetric Evaluation of Multimodal Human–Robot Interaction with Gaze and Standard Control." Symmetry 10, no. 12 (2018): 680. http://dx.doi.org/10.3390/sym10120680.
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Control of robot arms is often required in engineering and can be performed by using different methods. This study examined and symmetrically compared the use of a controller, eye gaze tracker and a combination thereof in a multimodal setup for control of a robot arm. Tasks of different complexities were defined and twenty participants completed an experiment using these interaction modalities to solve the tasks. More specifically, there were three tasks: the first was to navigate a chess piece from a square to another pre-specified square; the second was the same as the first task, but required more moves to complete; and the third task was to move multiple pieces to reach a solution to a pre-defined arrangement of the pieces. Further, while gaze control has the potential to be more intuitive than a hand controller, it suffers from limitations with regard to spatial accuracy and target selection. The multimodal setup aimed to mitigate the weaknesses of the eye gaze tracker, creating a superior system without simply relying on the controller. The experiment shows that the multimodal setup improves performance over the eye gaze tracker alone ( p < 0.05 ) and was competitive with the controller only setup, although did not outperform it ( p > 0.05 ).
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Luo, Donghai, Daobo Wang, Shengji Xia, and Tingting Bai. "A novel approach for visual tracking based on occlusion recognition." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 124–33. http://dx.doi.org/10.54097/hset.v7i.1027.
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Visual object tracking is an extremely challenging task. Many existing trackers cannot handle various challenges simultaneously. In this paper, we propose a novel tracking framework based on an occlusion recognition mechanism to improve the performance in occlusion situations. Firstly, we design an occlusion recognition mechanism based on patch pool and local correlation to describe the occlusion of objects in each frame of an image sequence. Secondly, taking advantage of the occlusion recognition mechanism, we construct a specific training set to train the filter. Thirdly, combining global correlation, we implement our own tracker based on the traditional discriminative correlation filters. Finally, we evaluate it on both OTB and VOT platforms, and the experimental results demonstrate that our design is advanced and effective.
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Sun, Kang. "New Compound Method for Target Recognition and Tracking." Applied Mechanics and Materials 273 (January 2013): 790–95. http://dx.doi.org/10.4028/www.scientific.net/amm.273.790.
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In this paper, we propose compound target detection and tracking method that combines Bayesian local features classification and global template tracking. During target initialization phase, we convert local features recognition problem into Semi-Naive Bayesian classification theory to avoid computing and matching complex high-dimension descriptor. During tracking, detector hands over tracking task to the template tracker, which imposes temporal continuity constraints across on-line frames in order to increase the robustness and efficiency of the results. In typical application scenarios, once the tracker loses target, it requires the detector for reinitialization. Experiment results confirm the efficiency of our approach at last.
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Reguindin, John Christopher, Reymund Sabay, and Dennis Madrigal. "DILG Negros Occidental Contact Tracer Information Management System." Technium BioChemMed 2, no. 4 (2021): 79–95. http://dx.doi.org/10.47577/biochemmed.v2i4.5389.
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COVID-19 pandemic reshapes the use of technology and innovation in an organization. Organizations are adopting and utilizing technologies in their day-to-day operations. Information Systems offer a wide range of solutions in terms of data management and interconnecting people with the use of a system. Its use was proven to enhance and improve the productivity of an organization in this pandemic. These promising results led the researcher to develop the Department of the Interior and Local Government (DILG) Negros Occidental Contact Tracer Information Management System (CTIMS). The CTIMS is a web application that enables DILG Negros Occidental Personnel to manage and facilitate all DILG Hired-Contact Tracers (CT) in the Province of Negros Occidental. The automated system made the data collection easy and error-free, streamlined reporting, and eliminated the time-consuming process of consolidating the Accomplishment Reports (ARs) from the Field and Provincial Offices. The document tracker feature replaced the manual entry in the logbook. It also improved the received and released document reporting. It improved the processes of the CT Task team compared to the manual operations and is expected to increase the productivity, effectivity, and efficiency of DILG Negros Occidental.
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Kar, Anuradha. "MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems." Vision 4, no. 2 (2020): 25. http://dx.doi.org/10.3390/vision4020025.
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Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.
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Liu, Liu, Anran Huang, Qi Wu, Dan Guo, Xun Yang, and Meng Wang. "KPA-Tracker: Towards Robust and Real-Time Category-Level Articulated Object 6D Pose Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3684–92. http://dx.doi.org/10.1609/aaai.v38i4.28158.
Full textAbstract:
Our life is populated with articulated objects. Current category-level articulation estimation works largely focus on predicting part-level 6D poses on static point cloud observations. In this paper, we tackle the problem of category-level online robust and real-time 6D pose tracking of articulated objects, where we propose KPA-Tracker, a novel 3D KeyPoint based Articulated object pose Tracker. Given an RGB-D image or a partial point cloud at the current frame as well as the estimated per-part 6D poses from the last frame, our KPA-Tracker can effectively update the poses with learned 3D keypoints between the adjacent frames. Specifically, we first canonicalize the input point cloud and formulate the pose tracking as an inter-frame pose increment estimation task. To learn consistent and separate 3D keypoints for every rigid part, we build KPA-Gen that outputs the high-quality ordered 3D keypoints in an unsupervised manner. During pose tracking on the whole video, we further propose a keypoint-based articulation tracking algorithm that mines keyframes as reference for accurate pose updating. We provide extensive experiments on validating our KPA-Tracker on various datasets ranging from synthetic point cloud observation to real-world scenarios, which demonstrates the superior performance and robustness of the KPA-Tracker. We believe that our work has the potential to be applied in many fields including robotics, embodied intelligence and augmented reality. All the datasets and codes are available at https://github.com/hhhhhar/KPA-Tracker.
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McFadden, Sharon M., Abhirami Vimalachandran, and Elizabeth Blackmore. "Factors affecting performance on a target monitoring task employing an automatic tracker." Ergonomics 47, no. 3 (2004): 257–80. http://dx.doi.org/10.1080/00140130310001629748.
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