A self-supervised training method using a neural Kalman filter and Sinkhorn assignment learns to associate detections across frames without identity labels, reaching state-of-the-art self-supervised scores on MOT17 and MOT20.
Ob- ject detection with discriminatively trained part-based models
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Learning a Neural Association Network for Self-supervised Multi-Object Tracking
A self-supervised training method using a neural Kalman filter and Sinkhorn assignment learns to associate detections across frames without identity labels, reaching state-of-the-art self-supervised scores on MOT17 and MOT20.