A high-quality trajectory generation method for the multi-person tracking

Abstract

Recently, Multi-Person Tracking (MPT) has experienced significant progress due to the emergence of innovative methods. However, congestion and occlusion persist as formidable challenges for MPT. In this study, we propose a Space Matching method, which systematically considers all the potential pedestrian detections within each spatial division. This method can effectively mitigate the erroneous matching problem and hence improve the matching performance. We enhance the update process of the Kalman filter by excluding distorted detections, which can realize a more accurate motion estimation. Further, we propose a Locally Weighted Interpolation method to reconstruct the missing bounding boxes on certain frames, which can significantly reduce the computation time, saving about 80% compared to the Gaussian process regression-based approach. With the help of these strategies, we can generate high-quality pedestrian trajectories, ultimately bringing state-of-the-art results on both MOT17 and MOT20 datasets.