Pedestrian detection method based on convolution nerve network

Abstract

One new pedestrian detection method integrating static high-level features and movement features based on convolution nerve network is proposed in this paper. During the phase of unsupervised deep learning of pedestrian features, the hierarchical static features of pedestrians are extracted from the low to the high with convolution nerve network; the pedestrian movement features are obtained through mean value approach of rectangular block pixel difference. During the logic regression recognition phase, static features and movement features are integrated. The results show that the pedestrian detection algorithm of convolution nerve network integrating movement features greatly improve the pedestrian detection performance under complicated background.