A Multi-Pedestrian Tracking Algorithm Based on Center Point Detection and Person Re-identification

  Objectives  In video-based multiple object tracking, the object detection and re-identification have a strong correlation. The existing methods generally train the object detection and re-identification networks separately, which makes the tracking speed fail to meet the real-time requirements. In this paper, we integrate the detection and re-identification into one network to accelerate the tracking process, and also solve the problems of identity switching and failure tracking.

  Methods  This paper develops a pedestrian motion model and obtains the optimal state estimation of pedestrians using center point detection. The person re-identification model with deep layer features uses the Mahalanobis distance and cosine distance to enhance the ability of person identification. And the Hungary algorithm is used for data online association, where the state estimation results become more accurate using Kalman filtering, and the unrelated lost objects are predicted by motion.

  Results  Experiments are conducted on MOT15, MOT16 and MOT17 datasets using the proposed algorithm and other multi-pedestrian tracking algorithms, and the multiple object tracking accuracy of tracking results using our proposed algorithm is 63.5, 72.4 and 70.9, respectively, and the identity F1-measure is optimal, with the real-time rate.

  Conclusions  The proposed algorithm can accelerate the tracking speed by network parameter sharing, and improve the recognition accuracy by person re-identification training.