YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID

TL;DR

YOLO11-JDE is a real-time multi-object tracking system that combines detection and self-supervised Re-ID, achieving high accuracy and efficiency without needing labeled identity data.

Contribution

The paper presents YOLO11-JDE, a novel multi-object tracking approach that integrates self-supervised Re-ID into YOLO11, reducing data labeling costs and improving speed and accuracy.

Findings

Outperforms existing JDE methods on MOT benchmarks

Runs at higher FPS with fewer parameters

Eliminates need for labeled identity datasets

Abstract

We introduce YOLO11-JDE, a fast and accurate multi-object tracking (MOT) solution that combines real-time object 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, eliminating 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. YOLO11-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.