Spatiotemporal multi-scale bilateral motion network for gait recognition

The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented block is proposed to explore motion description at the feature level. It can allow the classic 2D convolutional structure to have the capability to directly portray gait movement patterns while preventing costly computations on the estimation of optical flow. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly described at various levels of temporal resolution. Furthermore, the dynamic information is sensitive to inaccurate segmentation on the edge, so a correction block is devised to eliminate the segmentation noise of silhouettes for getting more precise gait modality. Subsequently, the temporal feature set and the spatial features are combined to comprehensively characterize gait processes. Extensive experiments are conducted on CASIA-B and OU-MVLP datasets, and the results achieve an outstanding identification performance, which has demonstrated the effectiveness of the proposed approach.