ByteTraX improves tracking accuracy and speed over ByteTrack
ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding
Computer Vision and Pattern Recognition
Summary
Tracking multiple objects in videos can be tricky because objects often get hidden or confused with each other. The authors improved a popular tracking method called ByteTrack by changing how objects are linked and when new tracks start. Their improved method, called ByteTraX, reduces mistakes where identities get mixed up and runs faster. They tested ByteTraX on many different datasets and found it works better and quicker while keeping tracking reliable.
What this means in practice
- •For video surveillance operators: Improve continuous monitoring by reducing mistakes in object identity switching and speeding up multi-object tracking in real-time systems.
- •For sports analytics teams: Provide more accurate and faster tracking of players and objects in sports videos for performance analysis and strategy development.
Authors
Thomas A. O'Shea-Wheller
Abstract
The ByteTrack algorithm is a widely used and computationally efficient multi-object tracking architecture. Its core innovation lies in the combination of lenient bounding box associations with tracklet similarity matching to robustly deal with object occlusions. However, this strategy is nevertheless vulnerable to erroneous track reclassification and identity switching, as detection confidence scores dictate association priority. To address this, I present a simple enhancement of the ByteTrack architecture, named ByteTraX, that optimises track continuity via a single unified matching threshold, while penalising identity switches through stringent track initiation criteria. This approach achieves consistently improved performance across a range of diverse benchmarks including GMOT-40, LC-MOT, SportsMOT, TeamTrack, DAMUNT, and DeepSea-MOT, while simultaneously increasing processing speed by >10%. Specifically, results demonstrate a >40% reduction in identity switches, accompanied by mean increases in HOTA of 3.6, IDF1 of 5.6, and FPS of 6.3. As such, adoption of the ByteTraX algorithm has the potential to substantially enhance tracking performance over the ByteTrack baseline, while retaining the efficiency needed for real-time deployment. To facilitate usage, I provide the source code, integration functionality for the YOLO family of object detection models, and deployment instructions via an open source repository.