结合双流网络和金字塔映射的步态识别.

At present, although gait recognition methods based on deep learning have made some progress, data collection and changes in gait appearance are still challenges to achieving accurate gait recognition. In order to improve the network ' s ability to capture temporal and spatial gait information, this paper proposed a two-stream network architecture based on gait contour flow and gait feature differential flow. The gait contour stream toke the gait contour map as input to extract the spatial gait information contained in the gait contour sequence, and the gait feature difference stream used the gait feature difference map as input to capture the dynamic information between adjacent gait frames . At the same time, to make full use of the global and local information in the gait sequence, this paper proposed a multi-scale pyramid mapping ( MPM) module and inserted it into each single-stream network to enhance the network ' s ability to extract global and local gait information. The average recognition accuracy of this method on the gait datasets CASIA-B and OU-MVLP reaches 87. 0% and 85 . 5%, respectively, which shows that the two-stream network architecture and MPM module can effectively capture the spatiotemporal gait information in the gait sequence. [ABSTRACT FROM AUTHOR]