DragonTrack: Transformer-Enhanced Graphical Multi-Person Tracking in Complex Scenarios
Bishoy M. Galoaa, Somaieh Amraee, Sarah Ostadabbas · 2025
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 Drag-onTrack 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, sur-passing the closest competitor with a score of 80.3. On MOT20, DragonTrack attains a HOTA score of 63.2, out-performing the next best method scoring 62.611The DragonTrack code is available at https://github.com/ostadabbas/DragonTrack. .