Pedestrian Re-Identification on the Basis of Dictionary Learning and Fisher Discrimination Sparse Representation

被引：0
|
作者：
Zhang J.-W. [1 ]
Lin W.-Z. [1 ]
Qiu L.-Q. [1 ]
机构：
[1] School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong
来源：
| 2017年 / South China University of Technology卷 / 45期
基金：
中国国家自然科学基金;
关键词：
Dictionary learning; Fisher discrimination; Pedestrian re-identification; Scatter; Sparse representation;
D O I：
10.3969/j.issn.1000-565X.2017.07.008
中图分类号：
学科分类号：
摘要：
In order to overcome the inadequate consideration of the existing dictionary learning taken into the connection of pedestrian features of different camera views, a new pedestrian re-identification method is proposed on the basis of dictionary learning and Fisher discrimination sparse representation. By considering the similar sparse representation of features of the same pedestrian in different scenes, the concept of pedestrian re-identification scatter function is put forward through adding a regularization term that constrains the sparse representation. The regularization term aims at maximizing the between-class scatter of the sparse representation of different pedestrians, and minimizing the within-class scatter of the sparse representation of the same pedestrian. Thus, sparse representation with strong discrimination ability can be obtained via dictionary learning. Experimental results on VIPeR, PRID 450s and CAVIAR4REID datasets indicate that the recognition rate of the proposed method is higher than that of other dictionary learning-based pedestrian re-identification methods. © 2017, Editorial Department, Journal of South China University of Technology. All right reserved.
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页码：55 / 62
页数：7
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