Weight-based sparse coding for multi-shot person re-identification
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- 作者：YanWei Zheng ; Hao Sheng ; BeiChen Zhang ; Jun Zhang…
- 关键词：smart city ; person re ; identification ; video surveillance ; multi ; shot ; weight ; based sparse coding ; 鏅烘収鍩庡競 ; 琛屼汉鍐嶈瘑鍒?/li> 瑙嗛鐩戞帶 ; 澶氶噸璇嗗埆 ; 鍔犳潈绋€鐤忕紪鐮?/li> 100104
- 刊名：SCIENCE CHINA Information Sciences
- 出版年：2015
- 出版时间：October 2015
- 年：2015
- 卷：58
- 期：10
- 页码：1-15
- 全文大小：1,196 KB
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Hao Sheng (1) (2)
BeiChen Zhang (1)
Jun Zhang (3)
Zhang Xiong (1)
1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
2. Shenzhen Key Laboratory of Data Vitalization, Research Institute in Shenzhen, Beihang University, Shenzhen, 518057, China
3. Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, 53201, USA - 刊物类别：Computer Science
- 刊物主题：Chinese Library of Science
Information Systems and Communication Service - 出版者：Science China Press, co-published with Springer
- ISSN：1869-1919
文摘
Person re-identification (Re-ID) is the problem of matching a person from different cameras based on appearance. It has interesting algorithm challenges and extensive practical applications. This paper presents a weight-based sparse coding approach for person re-identification. First, three hypotheses are introduced to achieve a linear combination of images based on sparse coding. Then, we convert the person re-identification problem into an optimization problem with sparse constraints. To reduce the influence of abnormal residuals caused by occlusion and body variation, a weight-based sparse coding approach is proposed to achieve the optimal weights by the ordering statistics of square residuals iteratively. Experiments on various public datasets for different multi-shot modalities have shown good performance of the proposed approach compared with other state-of-the-art ones (more than 42% and 34% at rank-1 on CAVIAR4REID and i-LIDS, respectively). Keywords smart city person re-identification video surveillance multi-shot weight-based sparse coding