@Article{cmc.2020.010283,
AUTHOR = {Xinliang Tang, Xing Sun, Zhenzhou Wang, Pingping Yu, Ning Cao, Yunfeng Xu},
TITLE = {Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1185--1198},
URL = {http://www.techscience.com/cmc/v64n2/39354},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010283}
}