Abstract
Even though recent advances in object tracking have shown notable results in tracking efficiency, many of these algorithms are not powerful enough for its adaptation to appearance changes caused by intrinsic and extrinsic factors. In this paper, a robust object tracking method based on multi-granularity sparse representation has been proposed to exploit not only the effectiveness of holistic and local features but also make use of the representation ability of multiple patches under different granularity. For the first part, contour templates have been introduced to combine with PCA basis vectors and square templates to enhance the observation model’s ability to resist the appearance changes of the target. For the second part, a novel block-division scheme is designed for multi-granularity sparsity analysis, which takes into account the joint representation ability of the target patches with different sizes. At last, in order to reduce tracking model’s drift phenomenon due to model update, an adaptive update mechanism is designed by combining occlusion ratio and incremental HOG feature. Both qualitative and quantitative evaluations have been conducted on OTB-2013 datasets to demonstrate that the proposed tracking algorithm outperforms several state-of-the-art methods.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of China under Grant 61703283, 61703169, 61806127, in part by the China Postdoctoral Science Foundation under Project 2016M590812, Project 2017T100645 and Project 2017M612736, in part by the Guangdong Natural Science Foundation under Project 2017A030310067, Project 2018A030310450 and Project 2018A030310451, in part by the National Engineering Laboratory for Big Data System Computing Technology, in part by the Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), in part by the Scientific Research Foundation of Shenzhen University under Project 2019049 and Project 860-000002110328, in part by the Research Foundation for Postdoctor Worked in Shenzhen under Project 707-00012210.
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Chu, H., Wen, J., Lai, Z. (2019). Robust Object Tracking Based on Multi-granularity Sparse Representation. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_12
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