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Mohamed, Islam, Ibrahim Elhenawy, Ahmed W. Sallam, Andrew Gatt, and Ahmad Salah. "A practical evaluation of correlation filter-based object trackers with new features." PLOS ONE 17, no. 8 (2022): e0273022. http://dx.doi.org/10.1371/journal.pone.0273022.
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Visual object tracking is a critical problem in the field of computer vision. The visual object tracker methods can be divided into Correlation Filters (CF) and non-correlation filters trackers. The main advantage of CF-based trackers is that they have an accepted real-time tracking response. In this article, we will focus on CF-based trackers, due to their key role in online applications such as an Unmanned Aerial Vehicle (UAV), through two contributions. In the first contribution, we proposed a set of new video sequences to address two uncovered issues of the existing standard datasets. The first issue is to create two video sequence that is difficult to be tracked by a human being for the movement of the Amoeba under the microscope; these two proposed video sequences include a new feature that combined background clutter and occlusion features in a unique way; we called it hard-to-follow-by-human. The second issue is to increase the difficulty of the existing sequences by increasing the displacement of the tracked object. Then, we proposed a thorough, practical evaluation of eight CF-base trackers, with the top performance, on the existing sequence features such as out-of-view, background clutters, and fast motion. The evaluation utilized the well-known OTB-2013 dataset as well as the proposed video sequences. The overall assessment of the eight trackers on the standard evaluation metrics, e.g., precision and success rates, revealed that the Large Displacement Estimation of Similarity transformation (LDES) tracker is the best CF-based tracker among the trackers of comparison. On the contrary, with a deeper analysis, the results of the proposed video sequences show an average performance of the LDES tracker among the other trackers. The eight trackers failed to capture the moving objects in every frame of the proposed Amoeba movement video sequences while the same trackers managed to capture the object in almost every frame of the sequences of the standard dataset. These results outline the need to improve the CF-based object trackers to be able to process sequences with the proposed feature (i.e., hard-to-follow-by-human).
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Hao, Zhaohui, Guixi Liu, Jiayu Gao, and Haoyang Zhang. "Robust Visual Tracking Using Structural Patch Response Map Fusion Based on Complementary Correlation Filter and Color Histogram." Sensors 19, no. 19 (2019): 4178. http://dx.doi.org/10.3390/s19194178.
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A part-based strategy has been applied to visual tracking with demonstrated success in recent years. Different from most existing part-based methods that only employ one type of tracking representation model, in this paper, we propose an effective complementary tracker based on structural patch response fusion under correlation filter and color histogram models. The proposed method includes two component trackers with complementary merits to adaptively handle illumination variation and deformation. To identify and take full advantage of reliable patches, we present an adaptive hedge algorithm to hedge the responses of patches into a more credible one in each component tracker. In addition, we design different loss metrics of tracked patches in two components to be applied in the proposed hedge algorithm. Finally, we selectively combine the two component trackers at the response maps level with different merging factors according to the confidence of each component tracker. Extensive experimental evaluations on OTB2013, OTB2015, and VOT2016 datasets show outstanding performance of the proposed algorithm contrasted with some state-of-the-art trackers.
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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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Li, Chenpu, Qianjian Xing, Zhenguo Ma, and Ke Zang. "MFCFSiam: A Correlation-Filter-Guided Siamese Network with Multifeature for Visual Tracking." Wireless Communications and Mobile Computing 2020 (December 23, 2020): 1–19. http://dx.doi.org/10.1155/2020/6681391.
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With the development of deep learning, trackers based on convolutional neural networks (CNNs) have made significant achievements in visual tracking over the years. The fully connected Siamese network (SiamFC) is a typical representation of those trackers. SiamFC designs a two-branch architecture of a CNN and models’ visual tracking as a general similarity-learning problem. However, the feature maps it uses for visual tracking are only from the last layer of the CNN. Those features contain high-level semantic information but lack sufficiently detailed texture information. This means that the SiamFC tracker tends to drift when there are other same-category objects or when the contrast between the target and the background is very low. Focusing on addressing this problem, we design a novel tracking algorithm that combines a correlation filter tracker and the SiamFC tracker into one framework. In this framework, the correlation filter tracker can use the Histograms of Oriented Gradients (HOG) and color name (CN) features to guide the SiamFC tracker. This framework also contains an evaluation criterion which we design to evaluate the tracking result of the two trackers. If this criterion finds the SiamFC tracker fails in some cases, our framework will use the tracking result from the correlation filter tracker to correct the SiamFC. In this way, the defects of SiamFC’s high-level semantic features are remedied by the HOG and CN features. So, our algorithm provides a framework which combines two trackers together and makes them complement each other in visual tracking. And to the best of our knowledge, our algorithm is also the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result. Comprehensive experiments are conducted on the Online Tracking Benchmark (OTB), Temple Color (TC128), Benchmark for UAV Tracking (UAV-123), and Visual Object Tracking (VOT) Benchmark. The results show that our algorithm achieves quite a competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.
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Woodham, Catherine A., William A. Sandham, and Tariq S. Durrani. "Error analysis for the seismic PDA tracker." GEOPHYSICS 60, no. 5 (1995): 1451–56. http://dx.doi.org/10.1190/1.1443879.
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A new method for assessing the accuracy of a seismic event tracking algorithm is presented. Currently, the accuracy of automatic event tracking algorithms is assessed by the interpreter without the aid of any tracker error analysis. It is clear that a method for mathematically analyzing the tracker accuracy is important, and the method described here enables an accurate assessment of the tracker confidence. The tracking of seismic events through 3-D data sets using probabilistic data association (PDA) is a recently developed technique. The method requires the correlation of information from trackers working in two perpendicular directions, and diagonally, and also from trackers working forward and backward through the data set. The information contained in the covariance matrix, which forms part of the standard Kalman filter model, may be used in the tracking of seismic events using PDA. The importance of this information is two‐fold—it gives an indication of the confidence of the tracker over a particular seismic event, and it may be used to improve the correlation of the tracker information. The relevant Kalman filter theory will be presented, and its use in analyzing the tracker errors and in improving the tracker correlation will be explained. It is clear from the examples presented that the tracker correlation, and hence the overall accuracy of the tracker, may be improved, and that the tracker confidence may be obtained.
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Zhou, Tongxue, Ming Zhu, Dongdong Zeng, and Hang Yang. "Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1605959.
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Visual tracking is one of the most important components in numerous applications of computer vision. Although correlation filter based trackers gained popularity due to their efficiency, there is a need to improve the overall tracking capability. In this paper, a tracking algorithm based on the kernelized correlation filter (KCF) is proposed. First, fused features including HOG, color-naming, and HSV are employed to boost the tracking performance. Second, to tackle the fixed template size, a scale adaptive scheme is proposed which strengthens the tracking precision. Third, an adaptive learning rate and an occlusion detection mechanism are presented to update the target appearance model in presence of occlusion problem. Extensive evaluation on the OTB-2013 dataset demonstrates that the proposed tracker outperforms the state-of-the-art trackers significantly. The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original KCF tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes.
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Kishore, M. Sankar, and K. Veerabhadra Rao. "Robust correlation tracker." Sadhana 26, no. 3 (2001): 227–36. http://dx.doi.org/10.1007/bf02703384.
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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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Zhang, Fei, Shiping Ma, Lixin Yu, Yule Zhang, Zhuling Qiu, and Zhenyu Li. "Learning Future-Aware Correlation Filters for Efficient UAV Tracking." Remote Sensing 13, no. 20 (2021): 4111. http://dx.doi.org/10.3390/rs13204111.
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In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at ∼49 FPS on a single CPU.
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Du, Chenjie, Mengyang Lan, Mingyu Gao, Zhekang Dong, Haibin Yu, and Zhiwei He. "Real-Time Object Tracking via Adaptive Correlation Filters." Sensors 20, no. 15 (2020): 4124. http://dx.doi.org/10.3390/s20154124.
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Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.
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You, Shaoze, Hua Zhu, Menggang Li, Yutan Li, and Chaoquan Tang. "Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments." Applied Sciences 13, no. 1 (2022): 568. http://dx.doi.org/10.3390/app13010568.
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In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.
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Chen, Fei, and Xiaodong Wang. "Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking." Symmetry 13, no. 9 (2021): 1665. http://dx.doi.org/10.3390/sym13091665.
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Recently, Discriminative Correlation Filters (DCF) have shown excellent performance in visual object tracking. The correlation for a computing response map can be conducted efficiently in Fourier domain by Discrete Fourier Transform (DFT) of inputs, where the DFT of an image has symmetry on the Fourier domain. To enhance the robustness and discriminative ability of the filters, many efforts have been devoted to optimizing the learning process. Regularization methods, such as spatial regularization or temporal regularization, used in existing DCF trackers aim to enhance the capacity of the filters. Most existing methods still fail to deal with severe appearance variations—in particular, the large scale and aspect ratio changes. In this paper, we propose a novel framework that employs adaptive spatial regularization and temporal regularization to learn reliable filters in both spatial and temporal domains for tracking. To alleviate the influence of the background and distractors to the non-rigid target objects, two sub-models are combined, and multiple features are utilized for learning of robust correlation filters. In addition, most DCF trackers that applied 1-dimensional scale space search method suffered from appearance changes, such as non-rigid deformation. We proposed a 2-dimensional scale space search method to find appropriate scales to adapt to large scale and aspect ratio changes. We perform comprehensive experiments on four benchmarks: OTB-100, VOT-2016, VOT-2018, and LaSOT. The experimental results illustrate the effectiveness of our tracker, which achieved a competitive tracking performance. On OTB-100, our tracker achieved a gain of 0.8% in success, compared to the best existing DCF trackers. On VOT2018, our tracker outperformed the top DCF trackers with a gain of 1.1% in Expected Average Overlap (EAO). On LaSOT, we obtained a gain of 5.2% in success, compared to the best DCF trackers.
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Xu, Tianyang, Zhen-Hua Feng, Xiao-Jun Wu, and Josef Kittler. "An accelerated correlation filter tracker." Pattern Recognition 102 (June 2020): 107172. http://dx.doi.org/10.1016/j.patcog.2019.107172.
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Peso, Danijel, Alfred Nischwitz, Siegfried Ippisch, and Paul Obermeier. "Kernelized correlation tracker on smartphones." Pervasive and Mobile Computing 35 (February 2017): 108–24. http://dx.doi.org/10.1016/j.pmcj.2016.06.013.
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Degroote, Laurent, Gilles Hamerlinck, Karolien Poels, et al. "Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study." JMIR mHealth and uHealth 8, no. 5 (2020): e16674. http://dx.doi.org/10.2196/16674.
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Background Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. Objective This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. Methods A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. Results Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. Conclusions This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.
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Zhou, Lifang, Hongmei Li, Weisheng Li, Bangjun Lei, and Lu Wang. "Collaborative correlation filters for real-time tracking with spatial constraint." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 03 (2019): 1950012. http://dx.doi.org/10.1142/s0219691319500127.
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Accurate scale estimation of the target plays an important role in object tracking. Most state-of-the-art methods estimate the target size by employing an exhaustive scale search. These methods can achieve high accuracy but suffer significantly from large computational cost. In this paper, we first propose an adaptive scale search strategy with the scale selection factor instead of an exhaustive scale search. This proposed strategy contributes to reducing computational costs by adaptive sampling. Furthermore, the boundary effects of correlation filters are suppressed by investigating background information so that the accuracy of the proposed tracker can be boosted. Experiments’ empirical evaluations of 61 challenging benchmark sequences demonstrate that the overall tracking performance of the proposed tracker is very successfully improved. Moreover, our method obtains the top rank in performance by outperforming 17 state-of-the-art trackers on OTB2013.
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Xu, Jingxiang, Xuedong Wu, Zhiyu Zhu, et al. "Scale-Adaptive Context-Aware Correlation Filter with Output Constraints for Visual Target Tracking." Mathematical Problems in Engineering 2020 (June 9, 2020): 1–15. http://dx.doi.org/10.1155/2020/4303725.
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Context-aware correlation filter tracker is one of the most advanced target trackers, and it has significant improvement in tracking accuracy and success rate compared with traditional trackers. However, because the complexity of background in the process of tracking can lead to inaccurate output response of target tracking, an accurate tracking model is difficult to be established. Moreover, the drift problem is easy to occur during the tracking process due to the imprecise tracking model, especially when the target has large area occlusion, fast motion, and deformation. Aiming at the drift problem in the target tracking process, a novel algorithm is proposed in this paper. The developed method derives the specific representation of constraint output by assuming that the output response is Gaussian distribution, and a variable update parameter is obtained based on the output constraint relationship at first, then the tracking filter is selectively updated with changeable update parameters and fixed update parameters, and finally, the target scale is updated with maximizing posterior probability distribution. The effectiveness of developed algorithm is verified by comparing with other trackers on OTB-50 and OTB-100 evaluation benchmark datasets, and the experimental results have shown that the suggested tracker has higher overall object tracking performance than other trackers.
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Liu, Liqiang, Tiantian Feng, Yanfang Fu, et al. "Learning Adaptive Spatial Regularization and Temporal-Aware Correlation Filters for Visual Object Tracking." Mathematics 10, no. 22 (2022): 4320. http://dx.doi.org/10.3390/math10224320.
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Recently, discriminative correlation filters (DCF) based trackers have gained much attention and obtained remarkable achievements for their high efficiency and outstanding performance. However, undesirable boundary effects occur when the DCF-based trackers suffer from challenging situations, such as occlusion, background clutters, fast motion, and so on. To address these problems, this work proposes a novel adaptive spatial regularization and temporal-aware correlation filters (ASTCF) model to deal with the boundary effects which occur in the correlation filters tracking. Firstly, our ASTCF model learns a more robust correlation filter template by introducing spatial regularization and temporal-aware components into the objective function. The adaptive spatial regularization provides a more robust appearance model to handle the large appearance changes at different times; meanwhile, the temporal-aware constraint can enhance the time continuity and consistency of this model. They make correlation filters model more discriminating, and also reduce the influence of the boundary effects during the tracking process. Secondly, the objective function can be transformed into three sub-problems with closed-form solutions and effectively solved via the alternating direction method of multipliers (ADMM). Finally, we compare our tracker with some representative methods and evaluate using three different benchmarks, including OTB2015, VOT2018 and LaSOT datasets, where the experimental results demonstrate the superiority of our tracker on most of the performance criteria compared with the existing 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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Masood, Mubashar, and Gulistan Raja. "An adaptive learning based aberrance repressed multi-feature integrated correlation filter for Visual Object Tracking (VOT)." Mehran University Research Journal of Engineering and Technology 43, no. 4 (2024): 14. http://dx.doi.org/10.22581/muet1982.2832.
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Target tracking via Correlation Filter (CF) is a hot research area of computer vision domain, and offers various credible benefits. Existing CF algorithms face challenges when there are target appearance variations due to background noise, scale and illumination changes, occlusion, and fast motion, which severely degrades the overall tracker performance. To get maximum benefits, an object tracker should perform well with the less computational burden in the presence of real time challenging situations. To address this issue, a novel visual object trackeris proposed based on multi feature fusion and adaptive learning technique with aberrance suppression. At first, multiple features i.e., Histogram of gradient (HOG), Color Naming (CN), saliency, and gray level intensities are combined using feature fusion technique. Further, based on the evaluation of final fused response map using Peak-to-Sidelobe Ratio (PSR), an adaptive learning strategy is integrated to improve the learning phase of tracker. Tracking results show that the proposed strategy beats the other modern CF trackers with Distance Precision (DP) scores of 88.2%, 85.9%, and 74.1% and 64.7% over OTB2013, OTB2015, and TempleColor128 and UAV123 datasets respectively.
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Yang, Hao, Yingqing Huang, and Zhihong Xie. "Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter." Sensors 19, no. 7 (2019): 1625. http://dx.doi.org/10.3390/s19071625.
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In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss–Seidel iteration. In addition, the average peak-response difference (APRD) is proposed to reflect the degree of target-occlusion according to the target response, and an adaptive Kalman filter is established to support the target redetection. The proposed tracker achieved a success plot performance of 67.8% with 5.1 fps on the standard datasets OTB2013.
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Fajar Baskoro Ag. "Implementasi Algoritma Correlation Tracker Untuk Sistem Pencarian Objek Menggunakan Library DLIB." Jurnal Ilmiah Teknik Informatika dan Komunikasi 3, no. 1 (2023): 110–17. http://dx.doi.org/10.55606/juitik.v3i1.406.
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Object tracking is a process to determine and follow the position of a desired moving object using a camera in real time. There are various algorithms in making object tracking systems, one of which is the correlation tracker algorithm in the DLIB library. This object tracking system uses Raspberry Pi 4 which contains a python program for object tracking. Pi Camera is used as a realtime video image input. The results of using the correlation tracker algorithm in the DLIB library, namely the object tracking process can run well. There are 5 objects that are tracked and as a result the tracking box is always around the object, whether the object is moving left, right, forward, and backward. As the object moves back and forth, the scale on the tracking box may change according to the object's size. Performance on the Raspberry Pi can be seen from the video framerate of 10.5 fps.
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Xu, Zhengjun, Detian Huang, Xiaoqian Huang, Jiaxun Song, and Hang Liu. "DLUT: Decoupled Learning-Based Unsupervised Tracker." Sensors 24, no. 1 (2023): 83. http://dx.doi.org/10.3390/s24010083.
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Unsupervised learning has shown immense potential in object tracking, where accurate classification and regression are crucial for unsupervised trackers. However, the classification and regression branches of most unsupervised trackers calculate object similarities by sharing cross-correlation modules. This leads to high coupling between different branches, thus hindering the network performance. To address the above issue, we propose a Decoupled Learning-based Unsupervised Tracker (DLUT). Specifically, we separate the training pipelines of different branches to unlock their inherent learning potential so that different branches can fully explore the focused feature regions of interest. Furthermore, we design independent adaptive decoupling-correlation modules according to the characteristics of each branch to obtain more discriminative and easily locatable feature response maps. Finally, to suppress the noise interference brought by unsupervised pseudo-label training and highlight the foreground object, we propose a novel suppression-ranking-based unsupervised training strategy. Extensive experiments demonstrate that our DLUT outperforms state-of-the-art unsupervised trackers.
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Bian, Ziyang, Tingfa Xu, Junjie Chen, Liang Ma, Wenjing Cai, and Jianan Li. "Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking." Remote Sensing 14, no. 21 (2022): 5299. http://dx.doi.org/10.3390/rs14215299.
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Most existing tracking methods based on discriminative correlation filters (DCFs) update the tracker every frame with a fixed learning rate. However, constantly adjusting the tracker can hardly handle the fickle target appearance in UAV tracking (e.g., undergoing partial occlusion, illumination variation, or deformation). To mitigate this, we propose a novel auto-learning correlation filter for UAV tracking, which fully exploits valuable information behind response maps for adaptive feedback updating. Concretely, we first introduce a principled target state estimation (TSE) criterion to reveal the confidence level of the tracking results. We suggest an auto-learning strategy with the TSE metric to update the tracker with adaptive learning rates. Based on the target state estimation, we further developed an innovative lost-and-found strategy to recognize and handle temporal target missing. Finally, we incorporated the TSE regularization term into the DCF objective function, which by alternating optimization iterations can efficiently solve without much computational cost. Extensive experiments on four widely-used UAV benchmarks have demonstrated the superiority of the proposed method compared to both DCF and deep-based trackers. Notably, ALCF achieved state-of-the-art performance on several benchmarks while running over 50 FPS on a single CPU. Code will be released soon.
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Wei, Jian, and Feng Liu. "Coupled-Region Visual Tracking Formulation Based on a Discriminative Correlation Filter Bank." Electronics 7, no. 10 (2018): 244. http://dx.doi.org/10.3390/electronics7100244.
Full textAbstract:
The visual tracking algorithm based on discriminative correlation filter (DCF) has shown excellent performance in recent years, especially as the higher tracking speed meets the real-time requirement of object tracking. However, when the target is partially occluded, the traditional single discriminative correlation filter will not be able to effectively learn information reliability, resulting in tracker drift and even failure. To address this issue, this paper proposes a novel tracking-by-detection framework, which uses multiple discriminative correlation filters called discriminative correlation filter bank (DCFB), corresponding to different target sub-regions and global region patches to combine and optimize the final correlation output in the frequency domain. In tracking, the sub-region patches are zero-padded to the same size as the global target region, which can effectively avoid noise aliasing during correlation operation, thereby improving the robustness of the discriminative correlation filter. Considering that the sub-region target motion model is constrained by the global target region, adding the global region appearance model to our framework will completely preserve the intrinsic structure of the target, thus effectively utilizing the discriminative information of the visible sub-region to mitigate tracker drift when partial occlusion occurs. In addition, an adaptive scale estimation scheme is incorporated into our algorithm to make the tracker more robust against potential challenging attributes. The experimental results from the OTB-2015 and VOT-2015 datasets demonstrate that our method performs favorably compared with several state-of-the-art trackers.
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Zhu, Wenqiu, Guang Zou, Qiang Liu, and Zhigao Zeng. "An Enhanced Visual Attention Siamese Network That Updates Template Features Online." Security and Communication Networks 2021 (August 30, 2021): 1–19. http://dx.doi.org/10.1155/2021/9719745.
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Recently, Siamese trackers have attracted extensive attention because of their simplicity and low computational cost. However, for most Siamese trackers, only a frame of the video sequence is used as the template, and the template is not updated in inference process, which makes the tracking success rate inferior to the trackers that can update the template online. In the current study, we introduce an enhanced visual attention Siamese network (ESA-Siam). The method is based on a deep convolutional neural network, which integrates channel attention and spatial self-attention to improve the discriminative ability of the tracker for positive and negative samples. Channel attention reflects different targets according to the response value of different channels to achieve better target representation. Spatial self-attention captures the correlation between two arbitrary positions to help locate the target. At the same time, a template search attention module is designed to implicitly update the template features online, which can effectively improve the success rate of the tracker when the target is interfered by the background. The proposed ESA-Siam tracker shows superior performance compared with 18 existing state-of-the-art trackers on five benchmark datasets including OTB50, OTB100, VOT2016, VOT2018, and LaSOT.
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Wang, Wenbin, Chao Liu, Bo Xu, Long Li, Wei Chen, and Yingzhong Tian. "Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism." Applied Sciences 10, no. 6 (2020): 2151. http://dx.doi.org/10.3390/app10062151.
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Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).
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Yang, Lingxiao, David Zhang, and Lei Zhang. "Learning a Visual Tracker from a Single Movie without Annotation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9095–102. http://dx.doi.org/10.1609/aaai.v33i01.33019095.
Full textAbstract:
The recent success of deep network in visual trackers learning largely relies on human labeled data, which are however expensive to annotate. Recently, some unsupervised methods have been proposed to explore the learning of visual trackers without labeled data, while their performance lags far behind the supervised methods. We identify the main bottleneck of these methods as inconsistent objectives between off-line training and online tracking stages. To address this problem, we propose a novel unsupervised learning pipeline which is based on the discriminative correlation filter network. Our method iteratively updates the tracker by alternating between target localization and network optimization. In particular, we propose to learn the network from a single movie, which could be easily obtained other than collecting thousands of video clips or millions of images. Extensive experiments demonstrate that our approach is insensitive to the employed movies, and the trained visual tracker achieves leading performance among existing unsupervised learning approaches. Even compared with the same network trained with human labeled bounding boxes, our tracker achieves similar results on many tracking benchmarks. Code is available at: https://github.com/ZjjConan/UL-Tracker-AAAI2019.
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Fu, Changhong, Fuling Lin, Yiming Li, and Guang Chen. "Correlation Filter-Based Visual Tracking for UAV with Online Multi-Feature Learning." Remote Sensing 11, no. 5 (2019): 549. http://dx.doi.org/10.3390/rs11050549.
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In this paper, a novel online learning-based tracker is presented for the unmanned aerial vehicle (UAV) in different types of tracking applications, such as pedestrian following, automotive chasing, and building inspection. The presented tracker uses novel features, i.e., intensity, color names, and saliency, to respectively represent both the tracking object and its background information in a background-aware correlation filter (BACF) framework instead of only using the histogram of oriented gradient (HOG) feature. In other words, four different voters, which combine the aforementioned four features with the BACF framework, are used to locate the object independently. After obtaining the response maps generated by aforementioned voters, a new strategy is proposed to fuse these response maps effectively. In the proposed response map fusion strategy, the peak-to-sidelobe ratio, which measures the peak strength of the response, is utilized to weight each response, thereby filtering the noise for each response and improving final fusion map. Eventually, the fused response map is used to accurately locate the object. Qualitative and quantitative experiments on 123 challenging UAV image sequences, i.e., UAV123, show that the novel tracking approach, i.e., OMFL tracker, performs favorably against 13 state-of-the-art trackers in terms of accuracy, robustness, and efficiency. In addition, the multi-feature learning approach is able to improve the object tracking performance compared to the tracking method with single-feature learning applied in literature.
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Chen, Hang, Weiguo Zhang, and Danghui Yan. "Learning Geometry Information of Target for Visual Object Tracking with Siamese Networks." Sensors 21, no. 23 (2021): 7790. http://dx.doi.org/10.3390/s21237790.
Full textAbstract:
Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the location of the sampling points in the search feature area is pre-fixed in cross-correlation operation, these trackers suffer from either background noise influence or missing foreground information. Moreover, the cross-correlation between the template and the search area neglects the geometry information of the target. In this paper, we propose a Siamese deformable cross-correlation network to model the geometric structure of target and improve the performance of visual tracking. We propose to learn an offset field end-to-end in cross-correlation. With the guidance of the offset field, the sampling in the search image area can adapt to the deformation of the target, and realize the modeling of the geometric structure of the target. We further propose an online classification sub-network to model the variation of target appearance and enhance the robustness of the tracker. Extensive experiments are conducted on four challenging benchmarks, including OTB2015, VOT2018, VOT2019 and UAV123. The results demonstrate that our tracker achieves state-of-the-art performance.
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Yang, Honghong, and Shiru Qu. "Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5894639.
Full textAbstract:
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
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Lee, Byung Cheol, Junfei Xie, Toyin Ajisafe, and Sung-Hee Kim. "How Are Wearable Activity Trackers Adopted in Older Adults? Comparison between Subjective Adoption Attitudes and Physical Activity Performance." International Journal of Environmental Research and Public Health 17, no. 10 (2020): 3461. http://dx.doi.org/10.3390/ijerph17103461.
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Wearable activity trackers can motivate older adults to engage in the recommended daily amount of physical activity (PA). However, individuals may not maintain their use of the trackers over a longer period. To investigate the attitudes of activity tracker adoption and their effects on actual PA performance, we conducted a three-month study. We gave activity trackers to 16 older adults and assessed attitudes on activity tracker adoption through a survey during the study period. We extracted participants’ PA measures, step counts, and moderate and vigorous physical activity (MVPA) times. We observed significant differences in adoption attitudes during the three different periods (χ2(2, 48) = 6.27, p < 0.05), and PA measures followed similar decreasing patterns (F(83, 1357) = 12.56, 13.94, p < 0.00001). However, the Pearson correlation analysis (r = 0.268, p = 0.284) and a Bland–Altman plot indicated a bias between two PA measures. Positive attitudes at the initial stage did not persist through the study period, and both step counts and length of MVPA time showed waning patterns in the study period. The longitudinal results from both measures demonstrated the patterns of old adults’ long-term use and adoption. Considering the accuracy of the activity tracker and older adults’ athletic ability, MVPA times are more likely to be a reliable measure of older adults’ long-term use and successful adoption of activity trackers than step counts. The results support the development of better activity tracker design guidelines that would facilitate long-term adoption among older adults.
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Ni, Jianjun, Xue Zhang, Pengfei Shi, and Jinxiu Zhu. "An Improved Kernelized Correlation Filter Based Visual Tracking Method." Mathematical Problems in Engineering 2018 (December 17, 2018): 1–12. http://dx.doi.org/10.1155/2018/6931020.
Full textAbstract:
Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.
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Zhang, Yihong, Shuai Li, Demin Li, et al. "Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism." Sensors 20, no. 18 (2020): 5210. http://dx.doi.org/10.3390/s20185210.
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Marine object tracking is critical for search and rescue activities in the complex marine environment. However, the complex marine environment poses a huge challenge to the effect of tracking, such as the variability of light, the impact of sea waves, the occlusion of other ships, etc. Under these complex marine environmental factors, how to design an efficient dynamic visual tracker to make the results accurate, real time and robust is particularly important. The parallel three-branch correlation filters for complex marine environmental object tracking based on a confidence mechanism is proposed by us. The proposed tracker first detects the appearance change and position change of the object by constructing parallel three-branch correlation filters, which enhances the robustness of the correlation filter model. Through the weighted fusion of response maps, the center position of the object is accurately located. Secondly, the Gaussian-triangle joint distribution is used to replace the original Gaussian distribution in the training phase. Finally, a verification mechanism of confidence metric is embedded in the filter update section to analyze the tracking effect of the current frame, and to update the filter sample from verification result. Thus, a more accurate correlation filter is trained to prevent model drift and achieve a good tracking effect. We found that the effect of various interferences on the filter is effectively reduced by comparing with other trackers. The experiments prove that the proposed tracker can play an outstanding role in the complex marine environment.
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ZHAO, Zuopeng, Hongda ZHANG, Yi LIU, et al. "Prediction-Based Scale Adaptive Correlation Filter Tracker." IEICE Transactions on Information and Systems E102.D, no. 11 (2019): 2267–71. http://dx.doi.org/10.1587/transinf.2019edl8101.
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Fan, Baojie, Yang Cong, Jiandong Tian, and Yandong Tang. "Reliable Multi-Kernel Subtask Graph Correlation Tracker." IEEE Transactions on Image Processing 29 (2020): 8120–33. http://dx.doi.org/10.1109/tip.2020.3009883.
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Wang, Junnan, Zhenhong Jia, Huicheng Lai, Jie Yang, and Nikola K. Kasabov. "A Multi-Information Fusion Correlation Filters Tracker." IEEE Access 8 (2020): 162022–40. http://dx.doi.org/10.1109/access.2020.3021235.
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Ji, Zhangjian, and Weiqiang Wang. "Correlation filter tracker based on sparse regularization." Journal of Visual Communication and Image Representation 55 (August 2018): 354–62. http://dx.doi.org/10.1016/j.jvcir.2018.06.017.
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Liu, Hang, and Bodong Li. "Target tracker with masked discriminative correlation filter." IET Image Processing 14, no. 10 (2020): 2227–34. http://dx.doi.org/10.1049/iet-ipr.2019.0881.
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Wang, Qiaochu, Faxue Liu, Bao Zhang, Jinghong Liu, Fang Xu, and Yulong Wang. "SiamCTCA: Cross-Temporal Correlation Aggregation Siamese Network for UAV Tracking." Drones 9, no. 4 (2025): 294. https://doi.org/10.3390/drones9040294.
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In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, and the manipulation between features exacerbates the loss of information, leading to the degradation of precision and robustness, especially in the field of UAV target tracking. In response to this, we propose a new lightweight Siamese-based tracker, SiamCTCA. Its innovative cross-temporal aggregated strategy and three feature correlation fusion networks play a key role, in which the Transformer multistage embedding achieves cross-branch information fusion with the help of the intertemporal correlation interactive vision Transformer modules to efficiently integrate different levels of features, and the feed-forward residual multidimensional fusion edge mechanism reduces information loss by introducing residuals to cope with dynamic changes in the search region; and the response significance filter aggregation network suppresses the shallow noise amplification problem of neural networks. The modules are confirmed to be effective after ablation and comparison experiments, indicating that the tracker exhibits excellent tracking performance, and with faster tracking speeds than other trackers, these can be better deployed in the field of a UAV as a platform.
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Zhou, Bin, and Tuo Wang. "Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking." Applied Sciences 9, no. 7 (2019): 1338. http://dx.doi.org/10.3390/app9071338.
Full textAbstract:
Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, traditional CF-based trackers have insufficient context information, and easily drift in scenes of fast motion or background clutter. Moreover, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented an adaptive context-aware (CA) and structural correlation filter for tracking. Firstly, we propose a novel context selecting strategy to obtain negative samples. Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local models. Finally, we introduce an adaptive updating scheme by using a fluctuation parameter. Extensive comprehensive experiments on object tracking benchmark (OTB)-100 datasets demonstrate that our proposed tracker performs favorably against several state-of-the-art trackers.
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Lin, Bin, Yunpeng Bai, Bendu Bai, and Ying Li. "Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement." Remote Sensing 14, no. 16 (2022): 4073. http://dx.doi.org/10.3390/rs14164073.
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Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently.
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Liu, Liduo, Yongji Long, Guoning Li, Ting Nie, Chengcheng Zhang, and Bin He. "Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation." Applied Sciences 13, no. 17 (2023): 9746. http://dx.doi.org/10.3390/app13179746.
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Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve this problem and enhance the inference speed of the tracker, fast and accurate visual tracking with a group convolution and pixel-level correlation based on a Siamese network is proposed. The algorithm incorporates multi-layer feature information on the basis of Siamese networks. We designed a multi-scale feature aggregated channel attention block (MCA) and a global-to-local-information-fused spatial attention block (GSA), which enhance the feature extraction capability of the network. The use of a pixel-level mutual correlation operation in the network to match the search region with the template region refines the bounding box and reduces background interference. Comparing our work with the latest algorithms, the precision and success rates on the UAV123, OTB100, LaSOT, and GOT10K datasets were improved, and our tracker was able to run at 40FPS, with a better performance in complex scenes such as those with occlusion, illumination changes, and fast-motion situations.
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Luo, Shanshan, Baoqing Li, Xiaobing Yuan, and Huawei Liu. "Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection." Applied Sciences 11, no. 4 (2021): 1963. http://dx.doi.org/10.3390/app11041963.
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The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.
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Li, Houjie, Shuangshuang Yin, Fuming Sun, and Fasheng Wang. "Face Tracking via Content Aware Correlation Filter." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 677–89. http://dx.doi.org/10.46300/9106.2021.15.76.
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Face tracking is an importance task in many computer vision based augment reality systems. Correlation filters (CFs) have been applied with great success to several computer vision problems including object detection, classification and tracking, but few CF-based methods are proposed for face tracking. As an essential research direction in computer vision, face tracking is very important in many human-computer applications. In this paper, we present a content aware CF for face tracking. In our work, face content refers to the locality sensitive histogram based foreground feature and the learning samples extracted from complex background. It means that both foreground and background information are considered in constructing the face tracker. The foreground feature is introduced into the objective function which could learn an efficient model to adapt to the face appearance variation. For evaluating the proposed face tracker, we build a dataset which contains 97 video sequences covering the 11 challenging attributes of face tracking. Extensive experiments are conducted on the dataset and the results demonstrate that the proposed face tracker shows superior performance to several state-of-the-art tracking algorithms.
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Su, Jia, Lihui Gao, Wei Li, Yu Xia, Ning Cao, and Ruichao Wang. "Fast Face Tracking-by-Detection Algorithm for Secure Monitoring." Applied Sciences 9, no. 18 (2019): 3774. http://dx.doi.org/10.3390/app9183774.
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This work proposes a fast face tracking-by-detection (FFTD) algorithm that can perform tracking, face detection and discrimination tasks. On the basis of using the kernelized correlation filter (KCF) as the basic tracker, multitask cascade convolutional neural networks (CNNs) are used to detect the face, and a new tracking update strategy is designed. The update strategy uses the tracking result modified by detector to update the filter model. When the tracker drifts or fails, the discriminator module starts the detector to correct the tracking results, which ensures the out-of-view object can be tracked. Through extensive experiments, the proposed FFTD algorithm is shown to have good robustness and real-time performance for video monitoring scenes.
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Xu, Qingyu, Yangliu Kuai, Junggang Yang, and Xinpu Deng. "Enhanced Real-Time RGB-T Tracking by Complementary Learners." Journal of Circuits, Systems and Computers 30, no. 10 (2021): 2150307. http://dx.doi.org/10.1142/s0218126621503072.
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This paper focuses on integrating information from RGB and thermal infrared modalities to perform RGB-T object tracking in the correlation filter framework. Our baseline tracker is Staple (Sum of Template and Pixel-wise LEarners), which combines complementary cues in the correlation filter framework with high efficiency. Given the input RGB and thermal videos, we utilize the baseline tracker due to its high performance in both of accuracy and speed. Different from previous correlation filter-based methods, we perform the fusion tracking at both the pixel-fusion and decision-fusion levels. Our tracker is robust to the dataset challenges, and due to the efficiency of FFT, our tracker can maintain high efficiency with superior performance. Extensive experiments on the RGBT234 dataset have demonstrated the effectiveness of our work.
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Lian, Zhichao, Changju Feng, Zhonggeng Liu, Chanying Huang, Chunshan Xu, and Jin Sun. "A Novel Scale Insensitive KCF Tracker Based on HOG and Color Features." Journal of Circuits, Systems and Computers 29, no. 11 (2020): 2050183. http://dx.doi.org/10.1142/s0218126620501832.
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Kernelized Correlation Filters (KCF) for visual tracking have received much attention due to their fast speed and outstanding performances in real scenarios. However, the KCF sometimes still fails to track the targets with different scales, and it may drift because the target response is fixed and the original histogram of orientation gradient (HOG) features cannot represent the targets well. In this paper, we propose a novel fast tracker, which is based on KCF and insensitive to scale changes by learning two independent correlation filters (CFs) where one filter is designed for position estimation and the other is for scale estimation. In addition, it can adaptively change the target response and multiple features are integrated to improve the performance for our tracker. Finally, we employ an adaptive high confidence filters updating scheme to avoid errors. Evaluated on the popular OTB50 and OTB100 datasets, our proposed trackers show superior performances in terms of efficiency and accuracy compared to the existing methods.
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Siddiqua, Ayesha, Amena Saher, and Sumera Sumera. "Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking." Indian Journal Of Science And Technology 17, no. 8 (2024): 741–50. http://dx.doi.org/10.17485/ijst/v17i8.1458.
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Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment. Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision
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Linlin, Yuan, and Yao Liu. "Sports Target Tracking Based on Discriminant Correlation Filter and Convolutional Residual Network." Wireless Communications and Mobile Computing 2022 (April 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/2981513.
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During the sports tracking process, a moving target often encounters sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose a convolution residual network model based on a discriminative correlation filter. The proposed tracking method uses discriminative correlation filters as basic convolutional layers in convolutional neural networks and then integrates feature extraction, response graph generation, and model updates into end-to-end convolutional neural networks for model training and prediction. Meanwhile, the introduction of residual learning responds to the model failure due to changes in the target appearance during the tracking process. Finally, multiple features are integrated such as HOG (histogram of oriented gradient), CN (color names), and histogram of local intensities for comprehensive feature representation, which further improve the tracking performance. We evaluate the performance of the proposed tracker on MultiSports datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers, and the effectiveness of the feature extraction of the convolutional residual network is verified.