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

• URL: http://arxiv.org/abs/2501.13710v1
• Date: Thu, 23 Jan 2025 14:38:40 GMT
• Title: YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
• Authors: Iñaki Erregue, Kamal Nasrollahi, Sergio Escalera,
• Abstract summary: 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. 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.
• Score: 38.27486095404261
• License:
• 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.

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