期刊
NEUROCOMPUTING
卷 74, 期 18, 页码 3823-3831出版社
ELSEVIER
DOI: 10.1016/j.neucom.2011.07.024
关键词
Visual tracking; Feature descriptor; Online learning; Contextural information; Monte Carlo sampling
资金
- National Natural Science Foundation of China (NSFC) [61002040, 60903115]
- NSFC-GuangDong [10171782619-2000007]
The goal of this paper is to review the state-of-the-art progress on visual tracking methods, classify them into different categories, as well as identify future trends. Visual tracking is a fundamental task in many computer vision applications and has been well studied in the last decades. Although numerous approaches have been proposed, robust visual tracking remains a huge challenge. Difficulties in visual tracking can arise due to abrupt object motion, appearance pattern change, non-rigid object structures, occlusion and camera motion. In this paper, we first analyze the state-of-the-art feature descriptors which are used to represent the appearance of tracked objects. Then, we categorize the tracking progresses into three groups, provide detailed descriptions of representative methods in each group, and examine their positive and negative aspects. At last, we outline the future trends for visual tracking research. (C) 2011 Elsevier B.V. All rights reserved.
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