Dongliang;Yang  Xianglei Xing;Changjiang Song

       Abstract:Aiming at the problem of gait recognition, a multi-view gait recognition method based on the human pose estimation model is proposed. This method can effectively solve the problem of low gait recognition rates caused by limited viewpoints. The VIBE method is used to extract human pose parameters of video frames, but there are errors in human pose parameters estimated by VIBE, and direct rotation with attitude parameters will inevitably cause greater errors. The attitude parameters were corrected in the design experiment. Finally, we used the Rodrigues rotation matrix to generate human pose parameters from other angles for VIBE to complete the conversion of character angles. The calibration network, including the attitude average and angle correction model, is designed. The attitude parameters under are input as the mean value as our final attitude parameters and then sent into the angle correction model to correct the root node. Finally, the corrected gait sequence is obtained. We can intuitively observe that the human model has obvious effects, and the accuracy of gait recognition is verified by Gaitset, which verifies that the model has good qualitative and quantitative effects. After correcting the correction network, we can generate gait sequences from other perspectives through the existing perspective. This method can well expand the perspective of the database and obtain more accurate gait sequence models from other perspectives.