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Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 5834846, 9 pages
Research Article

Reidentification of Persons Using Clothing Features in Real-Life Video

1Faculty of Engineering, Tokushima University, Tokushima 7708506, Japan
2Xian Jiao Tong University, No. 28, Xianning West Road, Xian, China

Correspondence should be addressed to Guodong Zhang

Received 16 August 2016; Revised 6 November 2016; Accepted 24 November 2016; Published 11 January 2017

Academic Editor: Qiushi Zhao

Copyright © 2017 Guodong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification.