Related papers: GaitMAST: Motion-Aware Spatio-Temporal Feature Learning Network for Cross-View Gait Recognition

GaitMAST: Motion-Aware Spatio-Temporal Feature Learning Network for Cross-View Gait Recognition

URL: http://arxiv.org/abs/2210.11817v1
Date: Fri, 21 Oct 2022 08:42:00 GMT
Title: GaitMAST: Motion-Aware Spatio-Temporal Feature Learning Network for Cross-View Gait Recognition
Authors: Jingqi Li, Jiaqi Gao, Yuzhen Zhang, Hongming Shan, Junping Zhang
Abstract summary: We propose GaitMAST, which can unleash the potential of motion-aware features. GitMAST preserves the individual's unique walking patterns well. Our model achieves an average rank-1 accuracy of 98.1%.
Score: 32.76653659564304
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security and so on. Existing gait recognition methods pay attention to extracting either spatial or spatiotemporal representations. However, they barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. In this paper, we propose a novel motion-aware spatiotemporal feature learning network for gait recognition, termed GaitMAST, which can unleash the potential of motion-aware features. In the shallow layer, specifically, we propose a dual-path frame-level feature extractor, in which one path extracts overall spatiotemporal features and the other extracts motion salient features by focusing on dynamic regions. In the deeper layers, we design a two-branch clip-level feature extractor, in which one focuses on fine-grained spatial information and the other on motion detail preservation. Consequently, our GaitMAST preserves the individual's unique walking patterns well, further enhancing the robustness of spatiotemporal features. Extensive experimental results on two commonly-used cross-view gait datasets demonstrate the superior performance of GaitMAST over existing state-of-the-art methods. On CASIA-B, our model achieves an average rank-1 accuracy of 94.1%. In particular, GaitMAST achieves rank-1 accuracies of 96.1% and 88.1% under the bag-carry and coat wearing conditions, respectively, outperforming the second best by a large margin and demonstrating its robustness against spatial variations.