YOLO11-JDE实现高效多目标跟踪，无需人工标注身份数据
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
- 在YOLO11s中加入自监督Re-ID分支，联合检测与特征提取
- 在MOT17和MOT20上达到领先性能，速度更快参数更少
- 适合需要实时性与低资源消耗的工业级跟踪场景
我们提出YOLO11-JDE，一种快速且准确的多目标跟踪（MOT）方法，将实时目标检测与自监督重识别（Re-ID）结合。通过在YOLO11s中引入专用的Re-ID分支，模型实现检测与嵌入联合学习（JDE），为每个检测结果生成外观特征。Re-ID分支在完全自监督设置下训练，同时完成检测任务，无需依赖昂贵的身份标注数据集。采用带困难正样本和半困难负样本挖掘策略的三元组损失，学习具有判别力的嵌入特征。数据关联通过定制化追踪实现，有效融合运动、外观和位置线索。YOLO11-JDE在MOT17和MOT20基准上表现优异，超越现有JDE方法，在帧率（FPS）上更优，参数量最多减少十倍。因此，该方法对实际应用极具吸引力。
原文摘要 · Abstract (English)
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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