Adaptive learning compressive tracking based on Markov location prediction

被引:0
|
作者
Zhou, Xingyu [1 ]
Fu, Dongmei [1 ]
Yang, Tao [1 ]
Shi, Yanan [1 ]
机构
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
object tracking; Markov; compressive tracking; confidence map; OBJECT TRACKING; FILTER;
D O I
10.1117/1.JEI.26.2.023026
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance. (C) 2017 SPIE and IS&T
引用
收藏
页数:9
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