P-IoU: Accurate Motion Prediction Based Data Association for Multi-object Tracking

Xinya Wu, Jinhua Xu

Abstract: Multi-object tracking in complex scenarios remains a challenging task due to objects’ irregular motions and indistinguishable appearances. Traditional methods often approximate the motion direction of objects solely based on their bounding box information, leading to cumulative noise and incorrect association. Furthermore, the lack of depth information in these methods can result in failed discrimination between foreground and background objects due to the perspective projection of the camera. To address these limitations, we propose a Pose Intersection over Union (P-IoU) method to predict the true motion direction of objects by incorporating body pose information, specifically the motion of the human torso. Based on P-IoU, we propose PoseTracker, a novel approach that combines bounding box IoU and P-IoU effectively during association to improve tracking performance. Exploiting the relative stability of the human torso and the confidence of keypoints, our method effectively captures the genuine motion cues, reducing identity switches caused by irregular movements. Experiments on the DanceTrack and MOT17 datasets demonstrate that the proposed PoseTracker outperforms existing methods. Our method highlights the importance of accurate motion prediction of objects for data association in MOT and provides a new perspective for addressing the challenges posed by irregular object motion.

External IDs:dblp:conf/iconip/WuX23