Robust Object Tracking via Information Theoretic Measures

doi:10.1007/s11633-020-1235-2

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	Robust Object Tracking via Information Theoretic Measures
	Wei-Ning Wang 2,3; Qi Li 1,2,3; Liang Wang 2,3
刊名	International Journal of Automation and Computing
	2020
卷号	17 期号:5 页码:652-666
关键词	Object tracking information theoretic measures correntropy template update robust to complex noises.
ISSN号	1476-8186
DOI	10.1007/s11633-020-1235-2
英文摘要	Object tracking is a very important topic in the field of computer vision. Many sophisticated appearance models have been proposed. Among them, the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of noise. However, in practice, the tracked objects are often corrupted by complex noises (e.g., partial occlusions, illumination variations) so that the original appearance-based trackers become less effective. This paper presents a correntropy-based robust holistic tracking algorithm to deal with various noises. Then, a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective function. Based on the proposed information theoretic algorithm, we design a simple and effective template update scheme for object tracking. Experimental results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42265]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Artificial Intelligence Research, Chinese Academy of Sciences, Qingdao 266300, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China 3.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Wei-Ning Wang,Qi Li,Liang Wang. Robust Object Tracking via Information Theoretic Measures[J]. International Journal of Automation and Computing,2020,17(5):652-666.
APA	Wei-Ning Wang,Qi Li,&Liang Wang.(2020).Robust Object Tracking via Information Theoretic Measures.International Journal of Automation and Computing,17(5),652-666.
MLA	Wei-Ning Wang,et al."Robust Object Tracking via Information Theoretic Measures".International Journal of Automation and Computing 17.5(2020):652-666.