Visual tracking with semi-supervised online weighted multiple instance learning

被引:14
作者
Wang, Zhihui [1 ]
Yoon, Sook [2 ]
Xie, Shan Juan [3 ]
Lu, Yu [1 ]
Park, Dong Sun [4 ]
机构
[1] Chonbuk Natl Univ, Dept Elect Engn, Jeonju 561756, South Korea
[2] Mokpo Natl Univ, Dept Multimedia, Jeonnam, South Korea
[3] Hangzhou Normal Univ, Inst Remote Sensing & Earth Sci, Hangzhou, Zhejiang, Peoples R China
[4] Chonbuk Natl Univ, Div Elect Engn, Jeonju 561756, South Korea
基金
新加坡国家研究基金会;
关键词
Multiple instance learning; Semi-supervised learning; Weak classifier; Unlabeled sample; Inconsistency function; OBJECT TRACKING; SELECTION;
D O I
10.1007/s00371-015-1067-1
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Adaptive discriminative tracking is a new research topic that has attracted broad attention due to its extensive application value. To take full advantage of the information about targets and their surrounding background, we propose a novel single object tracking-by-detection tracker in this paper, combining semi-supervised learning, multiple instance learning and the Bayesian theorem. The tracker uses a block-based inconsistency function of the labeled and unlabeled training samples in the selection of optimal weak classifiers during the parameter updating phase of each frame. Experimental results showed that the proposed tracker has excellent performance over other eight state-of-the-art trackers for thirteen open-access video sequences.
引用
收藏
页码:307 / 320
页数:14
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