DragonTrack: Transformer-Enhanced Graphical Multi-Person Tracking in Complex Scenarios

Bishoy Galoaa, Somaieh Amraee, Sarah Ostadabbas; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6373-6382

This paper introduces the dynamic robust adaptive graph-based tracker (DragonTrack) as a novel end-to-end framework for multi-person tracking (MPT) by integrating a detection transformer model for object detection and feature extraction with a graph convolutional network for re-identification. DragonTrack leverages encoded features from the transformer for precise subject matching and track maintenance while the graphical component processes these features alongside geometric data to predict subsequent positions of tracked people. This methodology aims to enhance tracking accuracy and reliability as evidenced by improvements in key metrics such as higher order tracking accuracy (HOTA) and multiple object tracking accuracy (MOTA). We quantitatively compare DragonTrack with state-of-the-art methods on MOT17 MOT20 and DanceTrack datasets in which DragonTrack outperforms other methods. In challenging scenarios such as DanceTrack DragonTrack achieves an impressive MOTA score of 93.4 significantly higher than the second-best SOTA method ByteTrack which achieves only 89.6. Similarly on MOT17 DragonTrack scores 82.0 in MOTA surpassing the closest competitor with a score of 80.3. On MOT20 DragonTrack attains a HOTA score of 63.2 outperforming the next best method scoring 62.6