GaitSlice: A gait recognition model based on spatio-temporal slice features

Abstract

Improving the performance of gait recognition under multiple camera views (i.e., cross-view gait recognition) and various conditions is urgent. From observation, we find that adjacent body parts are inter-related while walking, and each frame in a gait sequence possesses different degrees of semantic information. In this paper, we propose a novel model, GaitSlice, to analyze the human gait based on spatio-temporal slice features. Spatially, we design Slice Extraction Device (SED) to form top-down inter-related slice features. Temporally, we introduce Residual Frame Attention Mechanism (RFAM) to acquire and highlight the key frames. To better simulate reality, GaitSlice combines parallel RFAMs with inter-related slice features to focus on the features’ spatio-temporal information. We evaluate our model on CASIA-B and OU-MVLP gait datasets and compare it with six typical gait recognition models by using rank-1 accuracy. The results show that GaitSlice achieves high accuracy in gait recognition under cross-view and various walking conditions.