Pedestrian tracking by learning deep features

Abstract

Pedestrian tracking technique is now widely used in many intelligent systems, such as video surveillance, security regions. But many methods suffer from illumination, human posture or human appendant. With the development of Convolutional Neural Networks (CNNs), deep feature can be learned. In this paper, training images will be divided into subregions to reduce the influence of human appendant, such as bags. The remain regions are almost fixed regions. Then these fixed regions will be fed into our CNNs for learning deep features. In order to copy with different sizes of training images, an arbitrarily-sized pooling layer is developed in our CNN architecture. Then, these deeply-learned feature vector can be used in pedestrian recognition. In our work, optical flow is used for pedestrian tracking. Experimental results show our proposed method can achieve pedestrian tracking effectively.