Relevant bibliographies by topics / Visual tracking

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Visual tracking'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Visual tracking"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Visual tracking"

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Danelljan, Martin. "Visual Tracking." Thesis, Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105659.

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Visual tracking is a classical computer vision problem with many important applications in areas such as robotics, surveillance and driver assistance. The task is to follow a target in an image sequence. The target can be any object of interest, for example a human, a car or a football. Humans perform accurate visual tracking with little effort, while it remains a difficult computer vision problem. It imposes major challenges, such as appearance changes, occlusions and background clutter. Visual tracking is thus an open research topic, but significant progress has been made in the last few yea
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Wessler, Mike. "A modular visual tracking system." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11459.

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Klein, Georg. "Visual tracking for augmented reality." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614262.

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Salama, Gouda Ismail Mohamed. "Monocular and Binocular Visual Tracking." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/37179.

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Visual tracking is one of the most important applications of computer vision. Several tracking systems have been developed which either focus mainly on the tracking of targets moving on a plane, or attempt to reduce the 3-dimensional tracking problem to the tracking of a set of characteristic points of the target. These approaches are seriously handicapped in complex visual situations, particularly those involving significant perspective, textures, repeating patterns, or occlusion. This dissertation describes a new approach to visual tracking for monocular and binocular image sequences, and f
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Dehlin, Carl. "Visual Tracking Using Stereo Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153776.

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Visual tracking concerns the problem of following an arbitrary object in a video sequence. In this thesis, we examine how to use stereo images to extend existing visual tracking algorithms, which methods exists to obtain information from stereo images, and how the results change as the parameters to each tracker vary. For this purpose, four abstract approaches are identified, with five distinct implementations. Each tracker implementation is an extension of a baseline algorithm, MOSSE. The free parameters of each model are optimized with respect to two different evaluation strategies called no
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Salti, Samuele <1982&gt. "On-line adaptive visual tracking." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3735/1/samuele_salti_tesi.pdf.

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Visual tracking is the problem of estimating some variables related to a target given a video sequence depicting the target. Visual tracking is key to the automation of many tasks, such as visual surveillance, robot or vehicle autonomous navigation, automatic video indexing in multimedia databases. Despite many years of research, long term tracking in real world scenarios for generic targets is still unaccomplished. The main contribution of this thesis is the definition of effective algorithms that can foster a general solution to visual tracking by letting the tracker adapt to mutating worki
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Salti, Samuele <1982&gt. "On-line adaptive visual tracking." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3735/.

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Visual tracking is the problem of estimating some variables related to a target given a video sequence depicting the target. Visual tracking is key to the automation of many tasks, such as visual surveillance, robot or vehicle autonomous navigation, automatic video indexing in multimedia databases. Despite many years of research, long term tracking in real world scenarios for generic targets is still unaccomplished. The main contribution of this thesis is the definition of effective algorithms that can foster a general solution to visual tracking by letting the tracker adapt to mutating worki
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Delabarre, Bertrand. "Contributions to dense visual tracking and visual servoing using robust similarity criteria." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S124/document.

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Dans cette thèse, nous traitons les problèmes de suivi visuel et d'asservissement visuel, qui sont des thèmes essentiels dans le domaine de la vision par ordinateur. La plupart des techniques de suivi et d'asservissement visuel présentes dans la littérature se basent sur des primitives géométriques extraites dans les images pour estimer le mouvement présent dans la séquence. Un problème inhérent à ce type de méthode est le fait de devoir extraire et mettre en correspondance des primitives à chaque nouvelle image avant de pouvoir estimer un déplacement. Afin d'éviter cette couche algorithmique
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Arslan, Ali Erkin. "Visual Tracking With Group Motion Approach." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/4/1056100/index.pdf.

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An algorithm for tracking single visual targets is developed in this study. Feature detection is the necessary and appropriate image processing technique for this algorithm. The main point of this approach is to use the data supplied by the feature detection as the observation from a group of targets having similar motion dynamics. Therefore a single visual target is regarded as a group of multiple targets. Accurate data association and state estimation under clutter are desired for this application similar to other multi-target tracking applications. The group tracking approach is used with t
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Zhu, Biwen. "Visual Tracking with Deep Learning : Automatic tracking of farm animals." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240086.

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Automatic tracking and video of surveillance on a farm could help to support farm management. In this project, an automated detection system is used to detect sows in surveillance videos. This system is based upon deep learning and computer vision methods. In order to minimize disk storage and to meet the network requirements necessary to achieve the real-performance, tracking in compressed video streams is essential. The proposed system uses a Discriminative Correlation Filter (DCF) as a classifier to detect targets. The tracking model is updated by training the classifier with online learnin
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Books on the topic "Visual tracking"

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Lu, Huchuan, and Dong Wang. Online Visual Tracking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9.

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Zhao, Xin, Shiyu Hu, and Xu-Cheng Yin. Visual Object Tracking. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4558-9.

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Panin, Giorgio. Model-Based Visual Tracking. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470943922.

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MacCormick, John. Stochastic Algorithms for Visual Tracking. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0679-1.

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Panin, Giorgio. Model-based visual tracking: The OpenTL framework. Wiley, 2011.

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Xing, Weiwei, Weibin Liu, Jun Wang, et al. Visual Object Tracking from Correlation Filter to Deep Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6242-3.

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Campbell, Philip E. Visual analysis of a radio frequency tracking system for virtual environments. Naval Postgraduate School, 1999.

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Essig, Kai. Vision-based image retrieval (VBIR): A new eye-tracking based approach to efficient and intuitive image retrieval. VDM Verlag Dr. Müller, 2008.

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Gamito, Pedro Santos Pinto, and Pedro Joel Rosa. I see me, you see me: Inferring cognitive and emotional processes from gazing behaviour. Cambridge Scholars Publishing, 2014.

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Lee, Vincent C. E. Eye mouse system: Mouse control technique for detecting and tracking of eyes in visual images. UMIST, 1997.

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Book chapters on the topic "Visual tracking"

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Clark, Uraina. "Visual Tracking." In Encyclopedia of Clinical Neuropsychology. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1415.

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Clark, Uraina. "Visual Tracking." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1415-2.

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Clark, Uraina S. "Visual Tracking." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1415.

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Marchand, Eric. "Visual Tracking." In Encyclopedia of Robotics. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-41610-1_102-1.

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Duchowski, Andrew T. "Visual Attention." In Eye Tracking Methodology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57883-5_1.

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Duchowski, Andrew T. "Visual Psychophysics." In Eye Tracking Methodology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57883-5_3.

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Lu, Huchuan, and Dong Wang. "Correlation Tracking." In Online Visual Tracking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_6.

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Lu, Huchuan, and Dong Wang. "Tracking by Segmentation." In Online Visual Tracking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_5.

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Lu, Huchuan, and Dong Wang. "Introduction to Visual Tracking." In Online Visual Tracking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_1.

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Lu, Huchuan, and Dong Wang. "Visual Tracking Based on Sparse Representation." In Online Visual Tracking. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0469-9_2.

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Conference papers on the topic "Visual tracking"

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Wang, Futian, Jie Fang, and Siyuan Yang. "Visual Object Tracking via CLIP Based Visual Prompt Tuning." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064691.

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Roberts, J., and D. Charnley. "Attentive Visual Tracking." In British Machine Vision Conference 1993. British Machine Vision Association, 1993. http://dx.doi.org/10.5244/c.7.46.

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Wavering, Albert J., and Ronald Lumia. "Predictive visual tracking." In Optical Tools for Manufacturing and Advanced Automation, edited by David P. Casasent. SPIE, 1993. http://dx.doi.org/10.1117/12.150188.

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Wang, Shu, Huchuan Lu, and Guang Yang. "Complementary Visual Tracking." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116555.

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Kwon, Junseok, and Kyoung Mu Lee. "Visual tracking decomposition." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539821.

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"Advanced Visual Tracking." In Third IEEE and ACM International Symposium on Mixed and Augmented Reality. IEEE, 2004. http://dx.doi.org/10.1109/ismar.2004.10.

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Wei, Xing, Yifan Bai, Yongchao Zheng, Dahu Shi, and Yihong Gong. "Autoregressive Visual Tracking." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00935.

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Sarma, Prathusha K., and Tarunraj Singh. "A mixture distribution for visual foraging." In ETRA '14: Eye Tracking Research and Applications. ACM, 2014. http://dx.doi.org/10.1145/2578153.2578210.

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Du, Ruofei, Eric Lee, and Amitabh Varshney. "Tracking-Tolerant Visual Cryptography." In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019. http://dx.doi.org/10.1109/vr.2019.8797924.

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Ma, Y., S. Worrall, and A. M. Kondoz. "Depth assisted visual tracking." In 2009 10th Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). IEEE, 2009. http://dx.doi.org/10.1109/wiamis.2009.5031456.

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Reports on the topic "Visual tracking"

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Basu, Saikat, Malcolm Stagg, Robert DiBiano, Manohar Karki, Supratik Mukhopadhyay, and Jerry Weltman. An Agile Framework for Real-Time Visual Tracking in Videos. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada581034.

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Tannenbaum, Allen R. Distributed Systems for Problems in Robust Control and Visual Tracking. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387787.

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Bennur, Shubhapriya. Consumers Visual Search Behavior on the Websites: An Eye Tracking Approach. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1467.

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Tannenbaum, Allen R. Geometric PDE's and Invariants for Problems in Visual Control Tracking and Optimization. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada428955.

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Chen, J., W. E. Dixon, D. M. Dawson, and V. K. Chitrakaran. Visual Servo Tracking Control of a Wheeled Mobile Robot with a Monocular Fixed Camera. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada465705.

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Dutra, Lauren M., James Nonnemaker, Nathaniel Taylor, et al. Visual Attention to Tobacco-Related Stimuli in a 3D Virtual Store. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0036.2005.

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We used eye tracking to measure visual attention to tobacco products and pro- and anti-tobacco advertisements (pro-ads and anti-ads) during a shopping task in a three-dimensional virtual convenience store. We used eye-tracking hardware to track the percentage of fixations (number of times the eye was essentially stationary; F) and dwell time (time spent looking at an object; DT) for several categories of objects and ads for 30 adult current cigarette smokers. We used Wald F-tests to compare fixations and dwell time across categories, adjusting comparisons of ads by the number of each type of a
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D'Amico, Angela, Christopher Kyburg, and Rowena Carlson. Software Tools for Visual and Acoustic Real-Time Tracking of Marine Mammals: Whale Identification and Logging Display (WILD). Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada533470.

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Nelson, W. T., Robert S. Bolia, Chris A. Russell, Rebecca M. Morley, and Merry M. Roe. Head-Slaved Tracking in a See-Through HMD: The Effects of a Secondary Visual Monitoring Task on Performance and Workload. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada430665.

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Sun, He. Animated electronic storybook and children's Mother Tongue development: Tracing the process and the outcome with eye-tracking. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/22664.

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Storybook reading to children is considered an efficient way to provide a meaningful context for exposure to unfamiliar words and grammar (Weizman &amp; Snow, 2001). Nonetheless, children with limited language knowledge (e.g., child MTL learners in Singapore) may benefit less from the reading activities, due to the gap between their skills and those required for processing the narration. They often fail to derive the meaning of unknown words/grammar from the verbal context and consequently have trouble figuring out the story plots (Verhallen &amp; Bus, 2010). Children’s electronic storybook (e
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Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection us
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