Robust object tracking with adaptive feature selection

被引：0
作者：
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
来源：
Qi, Yuan-Chen | 1600年 / Northeast University卷 / 29期
关键词：
Adaptive updating; Object tracking; Occlusion detection; Online learning;
D O I：
10.13195/j.kzyjc.2013.1537
中图分类号：
学科分类号：
摘要：
In order to solve the tracking problem of video sequences in real-world scenarios, a robust tracking algorithm based on adaptive feature selection is proposed. Firstly, for the problem that the candidate features of the online AdaBoost algorithm are not robust, a construction mode of the candidate feature pool is proposed, which combines color and pyramid gradient orientation histogram features. Then, for the problem that classifiers are vulnerable to the influence of improper samples during the update, a process of occlusion detection is added at each frame after obtaining the tracking result to avoid the phenomena of drift. Lots of comparison experiments show that the proposed algorithm tracks the object accurately and reliably in realistic videos. ©, 2014, Northeast University. All right reserved.
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页码：2137 / 2143
页数：6
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