Robust object tracking based on adaptive feature selection

被引：1

作者：

机构：

[1] College of Information Science and Engineering, Northeastern University

来源：

Chen, D.-Y. | 1600年 / Asian Network for Scientific Information卷 / 12期

关键词：

Adaboost; Adaptive feature selection; Object tracking; Occlusion detection;

D O I：

10.3923/itj.2013.7325.7330

中图分类号：

学科分类号：

摘要：

In order to solve the video-based object tracking problem in complex dynamic scenes, a robust tracking algorithm based on adaptive feature selection was proposed in this paper. To address the poor robustness of candidates in the feature pool in the online Adaboost algorithm and the drift problem caused by the template updating, a new feature pool was built based on both color features and histogram of pyramidal gradient features. An occlusion detector is added after the tracking in the current frame to improve the reliability of the realtime updated template. Experimental results showed that the proposed algorithm has better performance against object deformation, pose transformation, illumination variance and occlusion. © 2013 Asian Network for Scientific Information.

引用

页码：7325 / 7330

页数：5

共 9 条

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