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

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 re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.

A partition approach for robust gait recognition based on gait template fusion

Gait recognition has significant potential for remote human identification,hut it is easily influenced by identity-unrelated factors such as clothing,carrying conditions,and view angles.Many gait templates have been presented that can effectively represent gait features.Each gait template has its advantages and can represent different prominent information.In this paper,gait template fusion is proposed to improve the classical representative gait template(such as a gait energy image)which represents incomplete information that is sensitive to changes in contour.We also present a partition method to reflect the different gait habits of different body parts of each pedestrian.The fused template is cropped into three parts(head,trunk,and leg regions)depending on the human body,and the three parts are then sent into the convolutional neural network to learn merged features.We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones.The results show good accuracy and robustness of the proposed method for gait recognition.