Pedestrian tracking in surveillance video based on modified CNN

摘要

With the prevalence of surveillance video, surveillance data can be used in a wide variety of applications where moving object detection, object recognition and pedestrian tracking has become a significant field of research. Especially for pedestrian tracking, it has become an urgent problem to be solved. This paper proposes a novel method based on convolutional neural network called Matching-Siamese network for pedestrian tracking. First, the pedestrians are detected from surveillance videos through Faster-R-CNN and are numbered sequentially. Second, Matching-Siamese network is designed by modifying the structure of the traditional Siamese network to calculate the similarity of two input images. Third, using the image similarity determines whether the probe image of the target pedestrian and each pedestrian images are of the same identity or not. Finally, we track the target pedestrian in all videos by using the identity of probe image and pedestrian images. The results in this paper show that the proposed method outperforms most popular algorithms in terms of accuracy, overlap rate and computational efficiency, especially in the circumstances of object disappearing and reappearing. In addition, our method could use a latest probe pedestrian image to accomplish its tracking in videos ranging from randomly selected time and regions well.