GNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
MOT20-04
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 (2 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:
