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Wireless Communications and Mobile Computing
Volume 2017, Article ID 4089505, 11 pages
https://doi.org/10.1155/2017/4089505
Research Article

Color Distribution Pattern Metric for Person Reidentification

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China

Correspondence should be addressed to Yingsheng Ye; moc.qq@2300953451

Received 18 July 2017; Accepted 27 November 2017; Published 18 December 2017

Academic Editor: Zhaolong Ning

Copyright © 2017 Yingsheng Ye 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.

Abstract

Accompanying the growth of surveillance infrastructures, surveillance IP cameras mount up rapidly, crowding Internet of Things (IoT) with countless surveillance frames and increasing the need of person reidentification (Re-ID) in video searching for surveillance and forensic fields. In real scenarios, performance of current proposed Re-ID methods suffers from pose and viewpoint variations due to feature extraction containing background pixels and fixed feature selection strategy for pose and viewpoint variations. To deal with pose and viewpoint variations, we propose the color distribution pattern metric () method, employing color distribution pattern () for feature representation and SVM for classification. Different from other methods, does not extract features over a certain number of dense blocks and is free from varied pedestrian image resolutions and resizing distortion. Moreover, it provides more precise features with less background influences under different body types, severe pose variations, and viewpoint variations. Experimental results show that our method achieves state-of-the-art performance on both 3DPeS dataset and ImageLab Pedestrian Recognition dataset with 68.8% and 79.8% rank 1 accuracy, respectively, under the single-shot experimental setting.