Spatio-Temporal Fusion Gait Recognition Method Combining Silhouette and Pose

Most of the existing gait recognition methods are contour-based gait recognition methods, however, contours are easily affected by occlusion, resulting in a decrease in recognition accuracy. In real monitoring scenarios, occlusion is almost inevitable, and improving the accuracy of gait recognition under occlusion is the premise that the algorithm can land in practical applications. Aiming at this problem, a spatio-temporal fusion gait recognition method combining silhouette and pose is proposed. Using the ability of pose to resist occlusion, a multi-modality spatial feature fusion module is designed, and the feature reuse strategy and modal fusion strategy are used to improve the information capacity of spatial features. A multi-scale temporal feature extraction module is designed to extract temporal information at different time scales using independent branches, and an attention-based feature fusion strategy is proposed to integrate temporal information adaptively. A spatial feature set branch is designed to improve the representation of spatial-temporal features in a deeply supervised manner. Experimental results on publicly available datasets show the effectiveness of the proposed method, and the model has good robustness under occlusion. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.