Multi Object Tracking On Mot16
Metrics
IDF1
MOTA
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | IDF1 | MOTA |
|---|---|---|
| quasi-dense-instance-similarity-learning | 67.1 | 69.8 |
| a-simple-baseline-for-multi-object-tracking | - | 74.9 |
| deft-detection-embeddings-for-tracking | - | 68.03 |
| online-multi-object-tracking-with | 71.1 | 74.2 |
| hoptrack-a-real-time-multi-object-tracking | - | 63.12 |
| learning-a-neural-solver-for-multiple-object-1 | 61.7 | 58.6 |
| near-online-multi-target-tracking-with | - | 46.4 |
| lmot-efficient-light-weight-detection-and | 72.3 | 73.2 |
| fusion-of-head-and-full-body-detectors-for | - | 47.8 |
| motr-end-to-end-multiple-object-tracking-with | 67.0 | 66.8 |
| joint-detection-and-multi-object-tracking | - | 66.7 |
| exploit-the-connectivity-multi-object | - | 49.2 |
| towards-real-time-multi-object-tracking | - | 64.4 |
| online-multi-object-tracking-with-dual | - | 46.1 |
| trajectory-factory-tracklet-cleaving-and-re | - | 48.2 |
| detection-recovery-in-online-multi-object | 73.5 | 76.8 |
| lifted-disjoint-paths-with-application-in-1 | 64.7 | 61.3 |
| pp-yoloe-an-evolved-version-of-yolo | - | 77.7 |
| track-to-detect-and-segment-an-online-multi | 64.7 | 70.1 |
| remots-self-supervised-refining-multi-object | - | 76.9 |
| spatial-temporal-graph-transformer-for | 76.8 | 76.7 |
| do-different-tracking-tasks-require-different | 71.8 | 74.7 |
| deepmot-a-differentiable-framework-for | - | 54.8 |
| real-time-multiple-people-tracking-with | - | 50.9 |