GNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
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Rendering of new sequences is currently deactivated due to heavy load.
Rendering of new sequences is currently deactivated due to heavy load.
Rendering of new sequences is currently deactivated due to heavy load.
Short name:
GNNMatch
Detector:
Public
Description:
Reference:
I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.
Last submitted:
February 23, 2021 (3 years ago)
Published:
February 23, 2021 at 15:37:20 CET
Submissions:
4
Project page / code:
Open source:
Yes
Hardware:
TITAN RTX
Runtime:
0.1 Hz
Benchmark performance:
| Sequence | MOTA | IDF1 | HOTA | MT | ML | FP | FN | Rcll | Prcn | AssA | DetA | AssRe | AssPr | DetRe | DetPr | LocA | FAF | ID Sw. | Frag |
| MOT20 | 54.5 | 49.0 | 40.2 | 407 (32.8) | 317 (25.5) | 9,522 | 223,611 | 56.8 | 96.9 | 37.0 | 43.8 | 44.0 | 58.7 | 45.9 | 78.4 | 82.0 | 2.1 | 2,038 (35.9) | 2,456 (43.3) |
Detailed performance:
| Sequence | MOTA | IDF1 | HOTA | MT | ML | FP | FN | Rcll | Prcn | AssA | DetA | AssRe | AssPr | DetRe | DetPr | LocA | FAF | ID Sw. | Frag |
| MOT20-04 | 74.7 | 61.9 | 49.5 | 331 | 54 | 3,720 | 64,662 | 76.4 | 98.3 | 41.6 | 59.1 | 49.0 | 63.4 | 62.1 | 79.9 | 82.4 | 1.8 | 830 | 1,041 |
| MOT20-06 | 32.0 | 29.1 | 24.5 | 31 | 130 | 2,666 | 86,996 | 34.5 | 94.5 | 22.9 | 26.4 | 28.0 | 43.4 | 27.5 | 75.3 | 80.7 | 2.6 | 676 | 810 |
| MOT20-07 | 52.3 | 47.5 | 38.5 | 31 | 20 | 460 | 15,165 | 54.2 | 97.5 | 36.0 | 41.7 | 42.9 | 59.5 | 43.8 | 78.9 | 82.7 | 0.8 | 159 | 185 |
| MOT20-08 | 22.8 | 23.2 | 20.8 | 14 | 113 | 2,676 | 56,788 | 26.7 | 88.6 | 21.5 | 20.3 | 27.1 | 43.0 | 21.3 | 70.6 | 80.1 | 3.3 | 373 | 420 |
Raw data:
