RSANet: Relative-sequence quality assessment network for gait recognition in the wild

Abstract

Gait recognition in the wild has received increasing attention since the gait pattern is hard to disguise and can be captured in a long distance. However, due to occlusions and segmentation errors, low-quality silhouettes are common and inevitable. To mitigate this low-quality problem, some prior arts propose absolute-single quality assessment models. Although these methods obtain a good performance, they only focus on the silhouette quality of a single frame, lacking consideration of the variation state of the entire sequence. In this paper, we propose a Relative-Sequence Quality Assessment Network, named RSANet. It uses the Average Feature Similarity Module (AFSM) to evaluate silhouette quality by calculating the similarity between one silhouette and all other silhouettes in the same silhouette sequence. The silhouette quality is based on the sequence, reflecting a relative quality. Furthermore, RSANet uses Multi-Temporal-Receptive-Field Residual Blocks (MTB) to extend temporal receptive fields without parameter increases. It achieves a Rank-1 accuracy of 75.2% on Gait3D, 81.8% on GREW, and 77.6% on BUAA-Duke-Gait datasets respectively. The code is available at https://github.com/PGZ-Sleepy/RSANet.