Gait recognition method of temporal–spatial HOG features in critical separation of Fourier correction points

Abstract

To solve the problem of low accuracy of gait recognition algorithm in video surveillance, a multi-shot gait recognition algorithm based on two-sided Fourier correction motion point estimation is proposed, which is based on temporal–spatial HOG feature template matching. First, in view of the features of video surveillance, the image point estimation reconfiguration algorithm is used, and a gait feature extraction algorithm is designed for the unmarked class separation temporal–spatial HOG feature template matching, which realizes the automatic recognition and extraction of the gait. Secondly, aiming at extracting the difference of feature motion angle under different lens and different angle of view, the angle correction is realized based on two-sided Fourier series, and the search algorithm of adaptive sequential forward floating selection is designed for high-dimensional feature space. Finally, the simulation results on Southampton test library show that the correct gait classification rate of the proposed algorithm can reach 96.3%, and the gait motion angle can be basically consistent in different gait cycles after angle correction.