Detecting pedestrians in surveillance videos based on convolutional neural network and motion

Abstract

Pedestrian detection is a fundamental computer vision task with many practical applications in robotics, video surveillance, autonomous driving, and automotive safety. However, it is still a challenging problem due to the tremendous variations in illumination, clothing, color, scale, and pose. The aim of this paper to present our dynamic pedestrian detector. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian motion patterns. Although the CNN has good generalization performance, the CNN classifier is time-consuming. Therefore, we propose a novel architecture to reduce the time of feature extraction and training. Occlusion handling is one of the most important problem in pedestrian detection. For occlusion handling, we propose a method, which consists of extensive part detectors. The main advantage of our algorithm is that it can be trained on weakly labeled data, i.e. it does not require part annotations in the pedestrian bounding boxes.