ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2015, Vol. 52 ›› Issue (1): 177-190.doi: 10.7544/issn1000-1239.2015.20130995

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Video Object Tracking Based on Appearance Models Learning

Zhang Huanlong1,2,Hu Shiqiang1,Yang Guosheng3   

  1. 1(School of Aeronautics and Astronautics,Shanghai Jiao Tong University, Shanghai 200240); 2(Department of Computer and Information Engineering, Luoyang Institute of Scicnce and Technology, Luoyang, Henan 471023); 2(School of Information Engineering, Minzu University of China, Beijing 100081)
  • Online:2015-01-01

Abstract: Visual tracking is an active reasch topic in the field of computer vision and has been well studied in the last decades. A key component for achieving robust tracking is the tracker’s capability of updating its internal representation of tragets to capture the varying appearance. Although numberous approaches have been proposed, many challenging problems still remain in designing an effective model of the appearance of tracked objects. In recent years, the methods of appearance model associated with statistical learning have been promoting the study for video object tracking. To help reader swiftly learn the rencent advances and trends so as to easily grasp the key problems of visual object tracking based on appearance models learning, a detailed review of the existing appearance learning models is provided. Here, the mechanism of the tracking algorithm based on appearance model learning is introduced firstly. Then the state-of-the-art feature descriptors are analyzed to show their different performance. Meanwhile, the tracking progress is categorized into three main groups, and the character of representative methods in each group are compared and analyzed in detail. Finally, the current research on the tracking methods based appearance model learning is summarized and classified, and the further application and research trend is discussed.

Key words: appearance model learning, sparse representation, metric learning, video tracking, semi-supervised learning

CLC Number: