GSC-ByteTrack: a motion-aware and scale-adaptive multi-object tracking framework
TL;DR
The proposed framework effectively balances detection sensitivity and association stability, providing a robust and efficient solution for UAV multi-object tracking in complex traffic scenarios.
Abstract
In the UAV traffic monitoring scene, vehicle targets usually have the characteristics of small scale, dense distribution and complex camera motion. These factors will reduce the detection reliability and interfere with the data association process, which can easily lead to trajectory breakage and identity switching problems. Aiming at the above problems, this paper proposes a unified detection and tracking framework, termed GSC-ByteTrack (Global Motion, Scale-Adaptive, and Class- Consistency ByteTrack). Firstly, an improved YOLOv8-P2 detection model is introduced to enhance the high-resolution feature expression ability of small targets. On this basis, three modules of global motion compensation, scale adaptive threshold division and category consistency constraint are designed to alleviate the influence of camera motion, improve the stability of small target correlation and reduce cross-category mismatch. Experiments conducted on the VisDrone2019- MOT dataset demonstrate that the proposed method achieves superior tracking performance compared with existing approaches, reaching 53.4% HOTA and 69.4% IDF1. Compared with the YOLOv8n + ByteTrack baseline, the proposed framework improves HOTA and IDF1 by 3.0 and 7.1 percentage points, respectively, indicating substantial gains in overall tracking accuracy and identity consistency. The proposed framework effectively balances detection sensitivity and association stability, providing a robust and efficient solution for UAV multi-object tracking in complex traffic scenarios.