Learning Local Feature Descriptors for Multiple Object Tracking
The present study aims at learning class-agnostic embedding, which is suitable for Multiple Object Tracking (MOT). We demonstrate that the learning of local feature descriptors could provide a sufficient level of generalization. Proposed embedding function exhibits on-par performance with its dedicated person re-identification counterparts in their target domain and outperforms them in others. Through its utilization, our solutions achieve state-of-the-art performance in a number of MOT benchmarks, which includes CVPR'19 Tracking Challenge.
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KITTI
HPatches
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Multiple Object Tracking | KITTI Tracking test | SRK ODESA | MOTA | 90.03 | # 3 |
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