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Journal articles on the topic 'Tracker physics'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Li, Chenpu, Qianjian Xing, and Zhenguo Ma. "HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter." Sensors 20, no. 7 (April 10, 2020): 2137. http://dx.doi.org/10.3390/s20072137.

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In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC’s disadvantages. One model contained the target’s prior color information, and the other the target’s prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram–Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker’s performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

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2

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 (September 26, 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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3

Claessens, Tom. "Analyzing Virtual Physics Simulations with Tracker." Physics Teacher 55, no. 9 (December 2017): 558–60. http://dx.doi.org/10.1119/1.5011834.

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4

Davergne, Thomas, Antsa Rakotozafiarison, Hervé Servy, and Laure Gossec. "Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?" Sensors 20, no. 17 (August 25, 2020): 4797. http://dx.doi.org/10.3390/s20174797.

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In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

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5

Manzari, V. "Physics performance with the ALICE silicon tracker." Journal of Instrumentation 10, no. 05 (May 6, 2015): C05005. http://dx.doi.org/10.1088/1748-0221/10/05/c05005.

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6

Tajima, Hiroyasu. "GLAST Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 569, no. 1 (December 2006): 140–43. http://dx.doi.org/10.1016/j.nima.2006.08.063.

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7

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 (July 6, 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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8

Zhao, Long, Mubarak Adam Ishag Mahmoud, Honge Ren, and Meng Zhu. "A Visual Tracker Offering More Solutions." Sensors 20, no. 18 (September 19, 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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9

PROCUREUR, S. "MICROMEGAS TRACKERS FOR HADRONIC PHYSICS." Modern Physics Letters A 28, no. 13 (April 30, 2013): 1340024. http://dx.doi.org/10.1142/s0217732313400245.

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Modern experiments in hadronic physics require excellent detection and tracking capabilities in very high flux environment. Probing the quark and gluon content of the nucleon indeed necessitates semi-inclusive or exclusive reactions with very small cross-sections. Micromegas detectors have therefore been rapidly identified as natural candidates to equip the hottest region of the COMPASS spectrometer at CERN. Benefitting from several years of smooth operation at COMPASS, the Micromegas have been proposed to equip the future central tracker of the CLAS12 experiment at the Jefferson Laboratory. This project triggered an important R&D on the bulk and resistive technologies and inspired several projects involving cylindrical trackers. This paper focuses on the design and performance of the COMPASS and CLAS12 Micromegas, and reviews the R&D made on the discharge reduction in large hadron fluxes.

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10

Xie, Jing, Erik Stensrud, and Torbjørn Skramstad. "Detection-Based Object Tracking Applied to Remote Ship Inspection." Sensors 21, no. 3 (January 23, 2021): 761. http://dx.doi.org/10.3390/s21030761.

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We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.

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11

Ferrère, D. "Development of a silicon tracker for neutrino physics." Nuclear Physics B - Proceedings Supplements 61, no. 3 (February 1998): 187–94. http://dx.doi.org/10.1016/s0920-5632(97)00560-4.

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12

Weber, Martin. "The CMS tracker." Nuclear Physics B - Proceedings Supplements 142 (May 2005): 430–33. http://dx.doi.org/10.1016/j.nuclphysbps.2005.01.073.

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13

Castro, Edwards Cerqueira de, Evandro Ottoni Teatini Salles, and Patrick Marques Ciarelli. "A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking." Sensors 21, no. 5 (March 9, 2021): 1903. http://dx.doi.org/10.3390/s21051903.

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

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14

Morselli, Aldo. "The GLAST Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 530, no. 1-2 (September 2004): 158–62. http://dx.doi.org/10.1016/j.nima.2004.05.065.

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15

Delforouzi, Ahmad, Bhargav Pamarthi, and Marcin Grzegorzek. "Training-Based Methods for Comparison of Object Detection Methods for Visual Object Tracking." Sensors 18, no. 11 (November 16, 2018): 3994. http://dx.doi.org/10.3390/s18113994.

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Object tracking in challenging videos is a hot topic in machine vision. Recently, novel training-based detectors, especially using the powerful deep learning schemes, have been proposed to detect objects in still images. However, there is still a semantic gap between the object detectors and higher level applications like object tracking in videos. This paper presents a comparative study of outstanding learning-based object detectors such as ACF, Region-Based Convolutional Neural Network (RCNN), FastRCNN, FasterRCNN and You Only Look Once (YOLO) for object tracking. We use an online and offline training method for tracking. The online tracker trains the detectors with a generated synthetic set of images from the object of interest in the first frame. Then, the detectors detect the objects of interest in the next frames. The detector is updated online by using the detected objects from the last frames of the video. The offline tracker uses the detector for object detection in still images and then a tracker based on Kalman filter associates the objects among video frames. Our research is performed on a TLD dataset which contains challenging situations for tracking. Source codes and implementation details for the trackers are published to make both the reproduction of the results reported in this paper and the re-use and further development of the trackers for other researchers. The results demonstrate that ACF and YOLO trackers show more stability than the other trackers.

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16

Elsasser, C. "The LHCb Silicon Tracker." Journal of Instrumentation 9, no. 01 (January 7, 2014): C01009. http://dx.doi.org/10.1088/1748-0221/9/01/c01009.

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17

Phillips, P. W. "ATLAS strip tracker stavelets." Journal of Instrumentation 7, no. 02 (February 13, 2012): C02028. http://dx.doi.org/10.1088/1748-0221/7/02/c02028.

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18

Yulkifli, Yulkifli, and Ramli Ramli. "The Use of Tracker Application to Enchance Physics Teachers in Senior High School in Making Laboratory Video." Pelita Eksakta 1, no. 1 (January 26, 2018): 31. http://dx.doi.org/10.24036/pelitaeksakta/vol1-iss1/9.

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During this time, practicum activities in the School have not gone well. In fact, practical activities can support the understanding of the theory or material Physics taught to students. In addition, the lab can also generate student motivation and passion, as well as develop the students' process skills by conducting experiments. Experimental activities are very useful for students if this activity has been planned and designed well by teachers. Currently, technological advances can help improve Physical practice in the School. One of them by using video based laboratory (VBL). VBL is a practical activity in the laboratory in the form of video-based educational software. Technical Guidance for Making Video-Based Laboratory Application Tracker for Physics Teacher in Teachers' Consultation in Subject (MGMP) of High School Physics (SMA) in Padang and Padang Panjang City ", This activity was held for 2 months with the activity stage is preparation, implementation and activity evaluation. Based on the planning, the activities have been successfully done well. Implementation of this activity by the way the teacher is guided using the application Tracker on Physics practicum activities. The output of this activity is the Digital Video Labaratory for teaching material with Tracker application for physics practicum activity, especially Regular Straight Motion (GLB), Straight Changed Motion (GLBB), Momentum and Impulse.

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19

Bijnens, Wouter, Jos Aarts, An Stevens, Darcy Ummels, and Kenneth Meijer. "Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly." Sensors 19, no. 24 (December 4, 2019): 5344. http://dx.doi.org/10.3390/s19245344.

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Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

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20

Eadkhong, T., R. Rajsadorn, P. Jannual, and S. Danworaphong. "Rotational dynamics with Tracker." European Journal of Physics 33, no. 3 (March 29, 2012): 615–22. http://dx.doi.org/10.1088/0143-0807/33/3/615.

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21

Malige, A., G. Korcyl, R. Lalik, P. Salabura, and N. Rathod. "Development of Forward Tracker." Journal of Physics: Conference Series 1667 (October 2020): 012028. http://dx.doi.org/10.1088/1742-6596/1667/1/012028.

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22

Stiller, Johannes. "Physics performance studies for the ALICE inner tracker upgrade." Journal of Physics: Conference Series 446 (September 19, 2013): 012052. http://dx.doi.org/10.1088/1742-6596/446/1/012052.

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23

Wayne, Mitchell R. "The D0 tracker upgrade and projections for B physics." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 351, no. 1 (November 1994): 77–83. http://dx.doi.org/10.1016/0168-9002(94)91068-5.

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24

Chen, Fei, and Xiaodong Wang. "Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking." Symmetry 13, no. 9 (September 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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25

Burger, W. J. "The AMS silicon tracker." Nuclear Physics B - Proceedings Supplements 113, no. 1-3 (December 2002): 139–46. http://dx.doi.org/10.1016/s0920-5632(02)01833-9.

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26

Bencze, G., M. Bosteels, R. Brenner, S. Czellar, K. Ekman, A. Hentinen, I. Hietanen, et al. "Silicon tracker for LHC." Nuclear Physics B - Proceedings Supplements 32 (May 1993): 172–80. http://dx.doi.org/10.1016/0920-5632(93)90022-x.

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27

Asrizal, Asrizal, Yohandri Yohandri, and Zulhendri Kamus. "Studi Hasil Pelatihan Analisis Video dan Tool Pemodelan Tracker pada Guru MGMP Fisika Kabupaten Agam." JURNAL EKSAKTA PENDIDIKAN (JEP) 2, no. 1 (May 24, 2018): 41. http://dx.doi.org/10.24036/jep/vol2-iss1/84.

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Experiment is important in physics to explain and describe a physical phenomenon and its characteristics. For this reason physics teachers should conduct the physics experiment activities to motivate students in learning and to improve scientific process skills, scientific attitudes, and student understanding in physics learning. However, in the experiment activities of physics were found many problems such as experiment equipment was insufficient, there is no equipment, equipment can�t operated well, and so on. An alternative solution to solve this problem was to do the video analysis and modeling tool tracker on physics teacher. The purpose of this research was to investigate the effects of the implementation the tracker video analysis and modeling tool on physics teachers. The research design was pretest and postest for one group sample. The sample of research was 26 physics teachers of Physics MGMP teacher in Agam district of West Sumatera. Instruments to collect the data consist of pretest and postest sheet, performance assessment sheet of video analysis product and questionnaires sheet of physics teachers. Data analysis techniques include descriptive statistical analysis, normality test, homogeneity test and paired comparison test for one group sample. From the data analysis, it can be stated that: 1). the average value of video analysis products from physics teachers is 85.10 and this value can be classified into very good category, 2). the average value of responses of physics teachers on training implementation of video analysis and tool modeling is 73.81 and this average value can be grouped into good category, and 3). the implementation of video analysis training of object motion video by using tracker software is effective to improve the understanding the physics MGMP teachers in Agam district on learning material of tracker video analysis and modeling tool, but the average value of understanding still in the low category.

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28

Kinchin, John. "Tracker demonstrates circular motion." Physics Education 47, no. 1 (December 22, 2011): 15–17. http://dx.doi.org/10.1088/0031-9120/47/1/f06.

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29

Yang, Linghui, Yuanlin Pan, Jiarui Lin, Yang Liu, Yue Shang, Shuo Yang, and Hanwen Cao. "Automatic Guidance Method for Laser Tracker Based on Rotary-Laser Scanning Angle Measurement." Sensors 20, no. 15 (July 27, 2020): 4168. http://dx.doi.org/10.3390/s20154168.

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Laser-tracking measurement systems (laser tracker) have been playing a critical role in large-scale 3D high-precision coordinate measurement. However, the existing visual guidance of laser trackers is still limited by the disadvantages of operator-dependence, small-angle view field, time-consuming laser-guided process. This paper presents an automatic guidance method for laser trackers based on the rotary-laser scanning angle measurement technology. In this method, a special target consisting of six photoelectric receivers and a retroreflector is integrated into the rotary-laser scanning transmitter’ coordinate systems. Real-time constraints calculated by the proposed method would provide the coordinates of the target in a laser tracker coordinates system for guidance. Finally, the experimental results verified the automatic re-establish of sightline can be realized in horizontal 360° angle field within tens of arc-seconds, and this method is robust against the fast movement of the target.

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Lehner, Frank. "The LHCb Inner Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 511, no. 1-2 (September 2003): 43–47. http://dx.doi.org/10.1016/s0168-9002(03)01748-0.

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Ermoline, Yuri. "LHCb Silicon Tracker infrastructure." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 518, no. 1-2 (February 2004): 309–12. http://dx.doi.org/10.1016/j.nima.2003.11.006.

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Affolder, T., D. Allspach, D. Ambrose, J. Bialek, W. Bokhari, M. Brozovic, M. Binkley, et al. "CDF Central Outer Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 526, no. 3 (July 2004): 249–99. http://dx.doi.org/10.1016/j.nima.2004.02.020.

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Needham, M. "The LHCb Silicon Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 530, no. 1-2 (September 2004): 23–27. http://dx.doi.org/10.1016/j.nima.2004.05.041.

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34

Adeva, B., M. Agari, C. Bauer, D. Baumeister, A. Bay, R. P. Bernhard, R. Bernet, et al. "The LHCb silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 546, no. 1-2 (July 2005): 76–80. http://dx.doi.org/10.1016/j.nima.2005.03.113.

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Mennea, M. S., I. Osborne, A. Regano, and G. Zito. "CMS tracker visualization tools." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 548, no. 3 (August 2005): 391–400. http://dx.doi.org/10.1016/j.nima.2005.03.167.

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List, Benno. "The H1 silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 549, no. 1-3 (September 2005): 33–36. http://dx.doi.org/10.1016/j.nima.2005.04.021.

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Voss, Helge. "The LHCb Silicon Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 549, no. 1-3 (September 2005): 44–48. http://dx.doi.org/10.1016/j.nima.2005.04.023.

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Tobin, Mark. "The LHCb Silicon Tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 732 (December 2013): 168–72. http://dx.doi.org/10.1016/j.nima.2013.07.019.

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Bernhard, Ralf. "The LHCb silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 596, no. 1 (October 2008): 17–20. http://dx.doi.org/10.1016/j.nima.2008.07.124.

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Cooper, W. E. "The D0 silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 598, no. 1 (January 2009): 41–45. http://dx.doi.org/10.1016/j.nima.2008.08.065.

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Pooth, Oliver. "The CMS silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 569, no. 1 (December 2006): 21–24. http://dx.doi.org/10.1016/j.nima.2006.09.052.

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Lipton, Ronald. "The D0 silicon tracker." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 418, no. 1 (November 1998): 85–90. http://dx.doi.org/10.1016/s0168-9002(98)00720-7.

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Zhu, Chengfei, Shan Jiang, Shuxiao Li, and Xiaosong Lan. "Efficient and Practical Correlation Filter Tracking." Sensors 21, no. 3 (January 25, 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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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 (July 24, 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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Tricomi, A. "Upgrade of the CMS tracker." Journal of Instrumentation 9, no. 03 (March 25, 2014): C03041. http://dx.doi.org/10.1088/1748-0221/9/03/c03041.

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Goh, J., M. S. Kim, Y. Ban, J. Cai, Q. Li, S. Liu, S. Qian, et al. "CMS RPC tracker muon reconstruction." Journal of Instrumentation 9, no. 10 (October 21, 2014): C10027. http://dx.doi.org/10.1088/1748-0221/9/10/c10027.

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Aad, G., B. Abbott, D. C. Abbott, A. Abed Abud, K. Abeling, D. K. Abhayasinghe, S. H. Abidi, et al. "The ATLAS Fast TracKer system." Journal of Instrumentation 16, no. 07 (July 1, 2021): P07006. http://dx.doi.org/10.1088/1748-0221/16/07/p07006.

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Du, Ming, Yan Ding, Xiuyun Meng, Hua-Liang Wei, and Yifan Zhao. "Distractor-Aware Deep Regression for Visual Tracking." Sensors 19, no. 2 (January 18, 2019): 387. http://dx.doi.org/10.3390/s19020387.

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In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

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Ji, Yuanjing. "Heavy flavor physics with the sPHENIX MAPS vertex tracker upgrade." Nuclear Physics A 1005 (January 2021): 121792. http://dx.doi.org/10.1016/j.nuclphysa.2020.121792.

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Salinas-Bueno, Iosune, Maria Francesca Roig-Maimó, Pau Martínez-Bueso, Katia San-Sebastián-Fernández, Javier Varona, and Ramon Mas-Sansó. "Camera-Based Monitoring of Neck Movements for Cervical Rehabilitation Mobile Applications." Sensors 21, no. 6 (March 23, 2021): 2237. http://dx.doi.org/10.3390/s21062237.

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Vision-based interfaces are used for monitoring human motion. In particular, camera-based head-trackers interpret the movement of the user’s head for interacting with devices. Neck pain is one of the most important musculoskeletal conditions in prevalence and years lived with disability. A common treatment is therapeutic exercise, which requires high motivation and adherence to treatment. In this work, we conduct an exploratory experiment to validate the use of a non-invasive camera-based head-tracker monitoring neck movements. We do it by means of an exergame for performing the rehabilitation exercises using a mobile device. The experiments performed in order to explore its feasibility were: (1) validate neck’s range of motion (ROM) that the camera-based head-tracker was able to detect; (2) ensure safety application in terms of neck ROM solicitation by the mobile application. Results not only confirmed safety, in terms of ROM requirements for different preset patient profiles, according with the safety parameters previously established, but also determined the effectiveness of the camera-based head-tracker to monitor the neck movements for rehabilitation purposes.

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