Recent advances and trends in visual tracking: A review

Yang, Hanxuan, Shao, Ling, Zheng, Feng, Wang, Liang and Song, Zhan (2011) Recent advances and trends in visual tracking: A review. Neurocomputing, 74 (18). pp. 3823-3831. ISSN 0925-2312

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Visual tracking; Feature descriptor; Online learning; Contextural information; Monte Carlo sampling
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Publisher
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 15:34
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22848

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