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Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images

Xinliang Tang1, Xing Sun1, Zhenzhou Wang1, Pingping Yu1, Ning Cao2, *, Yunfeng Xu3

1 Hebei University of Science and Technology, Shijiazhuang, 050000, China.
2 School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China.
3 Rutgers Business School-Newark, Washington Park, Newark, NJ 07102, USA.

* Corresponding Author: Ning Cao. Email: .

Computers, Materials & Continua 2020, 64(2), 1185-1198.


The appearance of pedestrians can vary greatly from image to image, and different pedestrians may look similar in a given image. Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging. Here, a pedestrian re-identification method based on the fusion of local features and gait energy image (GEI) features is proposed. In this method, the human body is divided into four regions according to joint points. The color and texture of each region of the human body are extracted as local features, and GEI features of the pedestrian gait are also obtained. These features are then fused with the local and GEI features of the person. Independent distance measure learning using the cross-view quadratic discriminant analysis (XQDA) method is used to obtain the similarity of the metric function of the image pairs, and the final similarity is acquired by weight matching. Evaluation of experimental results by cumulative matching characteristic (CMC) curves reveals that, after fusion of local and GEI features, the pedestrian reidentification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.


Cite This Article

X. Tang, X. Sun, Z. Wang, P. Yu, N. Cao et al., "Research on the pedestrian re-identification method based on local features and gait energy images," Computers, Materials & Continua, vol. 64, no.2, pp. 1185–1198, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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