Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.010283
Received 24 February 2020; Accepted 18 April 2020; Issue published 10 June 2020
Abstract
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.
Keywords
Cite This Article
APA Style
Tang, X., Sun, X., Wang, Z., Yu, P., Cao, N. et al. (2020). Research on the pedestrian re-identification method based on local features and gait energy images. Computers, Materials & Continua, 64(2), 1185-1198. https://doi.org/10.32604/cmc.2020.010283
Vancouver Style
Tang X, Sun X, Wang Z, Yu P, Cao N, Xu Y. Research on the pedestrian re-identification method based on local features and gait energy images. Comput Mater Contin. 2020;64(2):1185-1198 https://doi.org/10.32604/cmc.2020.010283
IEEE Style
X. Tang, X. Sun, Z. Wang, P. Yu, N. Cao, and Y. Xu "Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images," Comput. Mater. Contin., vol. 64, no. 2, pp. 1185-1198. 2020. https://doi.org/10.32604/cmc.2020.010283
Citations