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Journal articles on the topic 'Visual tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

ZANG, Chuantao, Yoshihide ENDO, and Koichi HASHIMOTO. "2P1-D20 GPU accelerating visual tracking." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _2P1—D20_1—_2P1—D20_4. http://dx.doi.org/10.1299/jsmermd.2010._2p1-d20_1.

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2

Roberts, J., and D. Charnley. "Parallel Visual Tracking." IFAC Proceedings Volumes 26, no. 1 (1993): 127–32. http://dx.doi.org/10.1016/s1474-6670(17)49287-1.

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3

Yuan, Heng, Wen-Tao Jiang, Wan-Jun Liu, and Sheng-Chong Zhang. "Visual node prediction for visual tracking." Multimedia Systems 25, no. 3 (2019): 263–72. http://dx.doi.org/10.1007/s00530-019-00603-1.

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4

Lou, Jianguang, Tieniu Tan, and Weiming Hu. "Visual vehicle tracking algorithm." Electronics Letters 38, no. 18 (2002): 1024. http://dx.doi.org/10.1049/el:20020692.

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5

Ming Yang, Ying Wu, and Gang Hua. "Context-Aware Visual Tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 7 (2009): 1195–209. http://dx.doi.org/10.1109/tpami.2008.146.

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6

Zhang, Lei, Yanjie Wang, Honghai Sun, Zhijun Yao, and Shuwen He. "Robust Visual Correlation Tracking." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/238971.

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Recent years have seen greater interests in the tracking-by-detection methods in the visual object tracking, because of their excellent tracking performance. But most existing methods fix the scale which makes the trackers unreliable to handle large scale variations in complex scenes. In this paper, we decompose the tracking into target translation and scale prediction. We adopt a scale estimation approach based on the tracking-by-detection framework, develop a new model update scheme, and present a robust correlation tracking algorithm with discriminative correlation filters. The approach wor

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7

Roberts, J. M., and D. Charnley. "Parallel attentive visual tracking." Engineering Applications of Artificial Intelligence 7, no. 2 (1994): 205–15. http://dx.doi.org/10.1016/0952-1976(94)90024-8.

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8

Wang, Hesheng, Yun-Hui Liu, and Weidong Chen. "Uncalibrated Visual Tracking Control Without Visual Velocity." IEEE Transactions on Control Systems Technology 18, no. 6 (2010): 1359–70. http://dx.doi.org/10.1109/tcst.2010.2041457.

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9

Shi, Liangtao, Bineng Zhong, Qihua Liang, Ning Li, Shengping Zhang, and Xianxian Li. "Explicit Visual Prompts for Visual Object Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4838–46. http://dx.doi.org/10.1609/aaai.v38i5.28286.

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How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus on designing a complicated appearance model or template updating strategy, while lacking the exploitation of context between consecutive frames and thus entailing the when-and-how-to-update dilemma. To address these issues, we propose a novel explicit visual prompts framework for visual tracking, dubbed EVPTrack. Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames withou

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10

Zhang, Yue, Huibin Lu, and Xingwang Du. "ROAM-based visual tracking method." Journal of Physics: Conference Series 1732 (January 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1732/1/012064.

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11

BĂNICĂ, Marian Valentin, Anamaria RĂDOI, and Petrișor Valentin PÂRVU. "ONBOARD VISUAL TRACKING FOR UAV’S." Scientific Journal of Silesian University of Technology. Series Transport 105 (December 1, 2019): 35–48. http://dx.doi.org/10.20858/sjsutst.2019.105.4.

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12

ZHANG, Tiansa, Chunlei HUO, Zhiqiang ZHOU, and Bo WANG. "Faster-ADNet for Visual Tracking." IEICE Transactions on Information and Systems E102.D, no. 3 (2019): 684–87. http://dx.doi.org/10.1587/transinf.2018edl8214.

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13

Wedel, Michel, and Rik Pieters. "Eye Tracking for Visual Marketing." Foundations and Trends® in Marketing 1, no. 4 (2006): 231–320. http://dx.doi.org/10.1561/1700000011.

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14

Chunhua Shen, Junae Kim, and Hanzi Wang. "Generalized Kernel-Based Visual Tracking." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 1 (2010): 119–30. http://dx.doi.org/10.1109/tcsvt.2009.2031393.

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15

Munich, M. E., and P. Perona. "Visual identification by signature tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 2 (2003): 200–217. http://dx.doi.org/10.1109/tpami.2003.1177152.

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16

Ma, Bo, Lianghua Huang, Jianbing Shen, Ling Shao, Ming-Hsuan Yang, and Fatih Porikli. "Visual Tracking Under Motion Blur." IEEE Transactions on Image Processing 25, no. 12 (2016): 5867–76. http://dx.doi.org/10.1109/tip.2016.2615812.

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17

Chen, Kai, and Wenbing Tao. "Convolutional Regression for Visual Tracking." IEEE Transactions on Image Processing 27, no. 7 (2018): 3611–20. http://dx.doi.org/10.1109/tip.2018.2819362.

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18

Pei, Zhijun, and Lei Han. "Visual Tracking Using L2 Minimization." MATEC Web of Conferences 61 (2016): 02020. http://dx.doi.org/10.1051/matecconf/20166102020.

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19

Zhou, Jiawei, and Shahram Payandeh. "Visual Tracking of Laparoscopic Instruments." Journal of Automation and Control Engineering 2, no. 3 (2014): 234–41. http://dx.doi.org/10.12720/joace.2.3.234-241.

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20

Yao, Rui, Guosheng Lin, Chunhua Shen, Yanning Zhang, and Qinfeng Shi. "Semantics-Aware Visual Object Tracking." IEEE Transactions on Circuits and Systems for Video Technology 29, no. 6 (2019): 1687–700. http://dx.doi.org/10.1109/tcsvt.2018.2848358.

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21

Kim, Minyoung. "Correlation-based incremental visual tracking." Pattern Recognition 45, no. 3 (2012): 1050–60. http://dx.doi.org/10.1016/j.patcog.2011.08.026.

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22

Li, Zhidong, Weihong Wang, Yang Wang, Fang Chen, and Yi Wang. "Visual tracking by proto-objects." Pattern Recognition 46, no. 8 (2013): 2187–201. http://dx.doi.org/10.1016/j.patcog.2013.01.020.

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23

Sergeant, D., R. Boyle, and M. Forbes. "Computer visual tracking of poultry." Computers and Electronics in Agriculture 21, no. 1 (1998): 1–18. http://dx.doi.org/10.1016/s0168-1699(98)00025-8.

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24

Bernardino, Alexandre, and José Santos-Victor. "Visual behaviours for binocular tracking." Robotics and Autonomous Systems 25, no. 3-4 (1998): 137–46. http://dx.doi.org/10.1016/s0921-8890(98)00043-8.

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25

Tannenbaum, Allen, Anthony Yezzi, and Alex Goldstein. "Visual Tracking and Object Recognition." IFAC Proceedings Volumes 34, no. 6 (2001): 1539–42. http://dx.doi.org/10.1016/s1474-6670(17)35408-3.

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26

Shao, Y., J. E. W. Mayhew, and Y. Zheng. "Model-driven active visual tracking." Real-Time Imaging 4, no. 5 (1998): 349–59. http://dx.doi.org/10.1016/s1077-2014(98)90004-3.

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27

Yun, Xiao, and Gang Xiao. "Spiral visual and motional tracking." Neurocomputing 249 (August 2017): 117–27. http://dx.doi.org/10.1016/j.neucom.2017.03.070.

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28

Xu, Weicun, Qingjie Zhao, and Dongbing Gu. "Fragmentation handling for visual tracking." Signal, Image and Video Processing 8, no. 8 (2012): 1639–49. http://dx.doi.org/10.1007/s11760-012-0406-1.

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29

Mei, Xue, Tianzhu Zhang, Huchuan Lu, Ming-Hsuan Yang, Kyoung Mu Lee, and Horst Bischof. "Special Issue on Visual Tracking." Computer Vision and Image Understanding 153 (December 2016): 1–2. http://dx.doi.org/10.1016/j.cviu.2016.11.001.

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30

Chli, Margarita, and Andrew J. Davison. "Active matching for visual tracking." Robotics and Autonomous Systems 57, no. 12 (2009): 1173–87. http://dx.doi.org/10.1016/j.robot.2009.07.010.

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31

Zhou, Yu, Xiang Bai, Wenyu Liu, and Longin Jan Latecki. "Similarity Fusion for Visual Tracking." International Journal of Computer Vision 118, no. 3 (2016): 337–63. http://dx.doi.org/10.1007/s11263-015-0879-9.

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32

王, 楠洋. "A Review of Visual Tracking." Computer Science and Application 08, no. 01 (2018): 35–42. http://dx.doi.org/10.12677/csa.2018.81006.

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33

Dai, Bo, Zhiqiang Hou, Wangsheng Yu, Feng Zhu, Xin Wang, and Zefenfen Jin. "Visual tracking via ensemble autoencoder." IET Image Processing 12, no. 7 (2018): 1214–21. http://dx.doi.org/10.1049/iet-ipr.2017.0486.

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34

BANDOPADHAY, AMIT, and DANA H. BALLARD. "Egomotion perception using visual tracking." Computational Intelligence 7, no. 1 (1991): 39–47. http://dx.doi.org/10.1111/j.1467-8640.1991.tb00333.x.

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35

Yokomichi, Masahiro, and Yuki Nakagama. "Multimodal MSEPF for visual tracking." Artificial Life and Robotics 17, no. 2 (2012): 257–62. http://dx.doi.org/10.1007/s10015-012-0050-4.

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36

Quinlan, P. "Visual tracking and feature binding." Ophthalmic and Physiological Optics 14, no. 4 (1994): 439. http://dx.doi.org/10.1016/0275-5408(94)90190-2.

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37

Drewes, Jan, Mingjie Gao, and Weina Zhu. "Tracking Visual Awareness in CFS." Journal of Vision 25, no. 9 (2025): 2390. https://doi.org/10.1167/jov.25.9.2390.

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38

Rao, Jinjun, Kai Xu, Jinbo Chen, et al. "Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach." Sensors 22, no. 2 (2022): 693. http://dx.doi.org/10.3390/s22020693.

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Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface mov

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39

Banu, Rubeena, and M. H. Sidram. "Window Based Min-Max Feature Extraction for Visual Object Tracking." Indian Journal Of Science And Technology 15, no. 40 (2022): 2047–55. http://dx.doi.org/10.17485/ijst/v15i40.1395.

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40

Peng, Chao, Danhua Cao, Yubin Wu, and Qun Yang. "Robot visual guide with Fourier-Mellin based visual tracking." Frontiers of Optoelectronics 12, no. 4 (2019): 413–21. http://dx.doi.org/10.1007/s12200-019-0862-0.

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41

Choi, Janghoon. "Global Context Attention for Robust Visual Tracking." Sensors 23, no. 5 (2023): 2695. http://dx.doi.org/10.3390/s23052695.

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Although there have been recent advances in Siamese-network-based visual tracking methods where they show high performance metrics on numerous large-scale visual tracking benchmarks, persistent challenges regarding the distractor objects with similar appearances to the target object still remain. To address these aforementioned issues, we propose a novel global context attention module for visual tracking, where the proposed module can extract and summarize the holistic global scene information to modulate the target embedding for improved discriminability and robustness. Our global context at

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42

Vihlman, Mikko, and Arto Visala. "Optical Flow in Deep Visual Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12112–19. http://dx.doi.org/10.1609/aaai.v34i07.6890.

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Single-target tracking of generic objects is a difficult task since a trained tracker is given information present only in the first frame of a video. In recent years, increasingly many trackers have been based on deep neural networks that learn generic features relevant for tracking. This paper argues that deep architectures are often fit to learn implicit representations of optical flow. Optical flow is intuitively useful for tracking, but most deep trackers must learn it implicitly. This paper is among the first to study the role of optical flow in deep visual tracking. The architecture of

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43

ZhongMing Liao and Azlan Ismail. "Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms." Journal of Smart Science and Technology 3, no. 1 (2023): 63–79. http://dx.doi.org/10.24191/jsst.v3i1.42.

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Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream

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44

Chen, Yuantao, Weihong Xu, Fangjun Kuang, and Shangbing Gao. "The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/925341.

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The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algo

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45

Battelli, Lorella, George A. Alvarez, Thomas Carlson, and Alvaro Pascual-Leone. "The Role of the Parietal Lobe in Visual Extinction Studied with Transcranial Magnetic Stimulation." Journal of Cognitive Neuroscience 21, no. 10 (2009): 1946–55. http://dx.doi.org/10.1162/jocn.2008.21149.

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Interhemispheric competition between homologous areas in the human brain is believed to be involved in a wide variety of human behaviors from motor activity to visual perception and particularly attention. For example, patients with lesions in the posterior parietal cortex are unable to selectively track objects in the contralesional side of visual space when targets are simultaneously present in the ipsilesional visual field, a form of visual extinction. Visual extinction may arise due to an imbalance in the normal interhemispheric competition. To directly assess the issue of reciprocal inhib

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46

Jung, Sangkil, Jinseok Lee, and Sangjin Hong. "Statistical Estimation and Adaptation for Visual Compensation in Object Tracking." International Journal of Distributed Sensor Networks 5, no. 5 (2009): 437–62. http://dx.doi.org/10.1080/15501320802581524.

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The multi-modal tracking model in [ 1 ] enables the on-the-fly error compensation with low complexity by adopting acoustic sensors for the main tracking task and visual sensors for correcting possible tracking errors. The visual compensation process in the model is indispensable to the accurate tracking task in a dynamic object movement. This article proposes an algorithm to approximate the successful visual compensation rate appearing in the multi-modal tracking system. The acoustic sampling interval of the object signal and the random occurrence of transmission delays of multi-modal data are

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47

Zhen, Xinxin, Shumin Fei, Yinmin Wang, and Wei Du. "A Visual Object Tracking Algorithm Based on Improved TLD." Algorithms 13, no. 1 (2020): 15. http://dx.doi.org/10.3390/a13010015.

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Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the res

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48

WANG, DONG, GANG YANG, and HUCHUAN LU. "TRI-TRACKING: COMBINING THREE INDEPENDENT VIEWS FOR ROBUST VISUAL TRACKING." International Journal of Image and Graphics 12, no. 03 (2012): 1250021. http://dx.doi.org/10.1142/s0219467812500210.

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Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel tri-tracking framework combining different views (different models using independent features) for robust object tracking. This new tracking framework exploits a hybrid discriminative generative model based on online semi-supervised learning. We only need the first frame for parameters initialization, and then the tracking process is

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49

Li, Kai, Lihua Cai, Guangjian He, and Xun Gong. "MATI: Multimodal Adaptive Tracking Integrator for Robust Visual Object Tracking." Sensors 24, no. 15 (2024): 4911. http://dx.doi.org/10.3390/s24154911.

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Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially when tracking dynamic objects like aircraft amidst rapid movements and environmental disturbances. This study introduces an innovative adaptive multimodal image object-tracking model that harnesses the capabilities of multispectral image sensors, combining infrared and visible light imagery to significantly enhance tracking accuracy and robustness

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50

Ruiz-Alzola, Juan, Carlos Alberola-López, and Jose-Ramón Casar Corredera. "Model-based stereo-visual tracking: Covariance analysis and tracking schemes." Signal Processing 80, no. 1 (2000): 23–43. http://dx.doi.org/10.1016/s0165-1684(99)00109-7.

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