Robust object tracking via sparsity-based collaborative model
Abstract
In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
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Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560 · see citing works
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100 · see citing works
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061 · see citing works
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Online Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138 · see citing works
Self-citationDiscriminative Scale Space Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2609928 · Cited by 1,315 · see citing works
Discriminative Correlation Filter with Channel and Spatial Reliability
Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153 · see citing works
Encoding Color Information for Visual Tracking: Algorithms and Benchmark
Liang, P., Blasch, E., & Ling, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2482905 · Cited by 726 · see citing works
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705 · see citing works
Sparse coding based visual tracking: Review and experimental comparison
Zhang, S., Yao, H., Sun, X., & Lu, X. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.006 · Cited by 284 · see citing works
Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking
Chen, K., & Tao, W. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2757061 · Cited by 94 · see citing works
Regional deep learning model for visual tracking
Wu, G., Lu, W., Gao, G., Zhao, C., & Liu, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.10.064 · Cited by 78 · see citing works
Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
Shin, J., Kim, H., Kim, D., & Paik, J. (2020) · Applied Sciences · doi:10.3390/app10020713 · Cited by 42 · see citing works
Unsupervised detection and tracking of moving objects for video surveillance applications
Elafi, I., Jedra, M., & Zahid, N. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.08.008 · Cited by 37 · see citing works
On the improvement of foreground–background model‐based object tracker
Hanif, M. S., Ahmad, S., & Khurshid, K. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0487 · Cited by 14 · see citing works
Multiple Cues-Based Robust Visual Object Tracking Method
Khan, B., Jalil, A., Ali, A., Alkhaledi, K., Mehmood, K., Cheema, K. M., Murad, M., Tariq, H., & El-Sherbeeny, A. M. (2022) · Electronics · doi:10.3390/electronics11030345 · Cited by 5 · see citing works
- Authors
- Zhong, W., Lu, H., & Yang, M.-H.
- Affiliations
- Dalian University of Technology (CN)
- University of California, Merced (US)
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 1838-1845
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2016075127
- Cited by
- 990
- References
- 3
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 6 · 2022: 22 · 2021: 29 · 2020: 48 · 2019: 91 · 2018: 126 · 2017: 156 · 2016: 186 · 2015: 176 · 2014: 99 · 2013: 39 · 2012: 7
- FWCI
- 72.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Science Foundation (1149783, CAREER, 1152576)
- National Natural Science Foundation of China (61071209)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- object tracking, appearance changes, local representation, occlusion handling, template updating, drift reduction
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