YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
Iñaki Erregue, Kamal Nasrollahi, Sérgio Escalera · 2025
We introduce YOLO11-JDE, a fast and accurate multi-object tracking (MOT) solution that combines real-time ob-ject detection with self-supervised Re-Identification (Re-ID). By incorporating a dedicated Re-ID branch into YOLO11s, our model performs Joint Detection and Embedding (JDE), generating appearance features for each detection. The Re-ID branch is trained in a fully self-supervised setting while simultaneously training for detection, elimi-nating the need for costly identity-labeled datasets. The triplet loss, with hard positive and semi-hard negative mining strategies, is used for learning discriminative embeddings. Data association is enhanced with a custom tracking implementation that successfully integrates motion, appearance, and location cues. YOLOII-JDE achieves competitive results on MOT17 and MOT20 benchmarks, surpassing existing JDE methods in terms of FPS and using up to ten times fewer parameters. Thus, making our method a highly attractive solution for real-world applications. The code is publicly available at https://github.com/inakierregueab/YOLO11-JDE.