A Novel Particle Filter based Object Tracking Framework via the Combination of State and Observation Optimization

被引：0

作者：

Luo, Xudong ^{[1
]}

Ye, Long ^{[1
]}

Zhong, Wei ^{[1
]}

Zhang, Qin ^{[1
]}

机构：

[1] Commun Univ China, Dept Elect Informat Engn, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER, NETWORKS AND COMMUNICATION ENGINEERING (ICCNCE 2013) | 2013年 / 30卷

关键词：

particle filter; object tracking; feature optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using particle filter to figure visual object tracking, a key problem is to choose appropriate image features as the observation model. In this paper, we present a novel particle filter based object tracking framework via the combination of state and observation optimization. We apply the technique to articulated human movement tracking. Result demonstrates the effectiveness of our method in solving the tracking problem like self-occlusion and cluttered background.

引用

页码：487 / 490

页数：4

共 9 条

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