Research on the design of gait recognition algorithm in multi-view scene based on deep learning

Abstract

Surveillance scenes and pedestrian retrieval and recognition in videos gradually play an increasingly important role. Due to the long distance between the camera and pedestrians, the commonly used biometric identification methods such as face recognition and iris recognition cannot be applied to actual scenes. middle. The gait is a biological feature that is not easy to hide, not easy to camouflage, and can be obtained from a long distance, so it can be effectively used in surveillance and pedestrian retrieval and recognition in videos. Can be used as a feature to distinguish individuals. This paper proposes a framework for gait recognition based on Autoencoder, which can be uniformly converted to an angle that is conducive to retrieval and recognition without knowing the angle between the pedestrian and the camera in advance. The accuracy of gait recognition in the case of angle. The framework takes the characteristic gait energy map of commonly used gait recognition as input, and extracts better features to express the pedestrian’s gait. This article is tested on the gait database dataset B collected by the Institute of Dynamics of the Chinese Academy of Sciences, that is, the public database of CAS worker A GaitDatabase B. The experimental results show that the framework can effectively solve the three common challenges in gait recognition, which are multi-view, carrying items and clothing changes, and achieve good results.