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.
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.
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.
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:
March 07, 2021 (3 years ago)
Published:
March 02, 2021 at 06:22:09 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 |
| MOT15 | 46.7 | 43.2 | 35.4 | 157 (21.8) | 203 (28.2) | 6,643 | 25,311 | 58.8 | 84.5 | 31.4 | 40.8 | 44.2 | 49.3 | 46.3 | 66.6 | 79.6 | 1.1 | 820 (0.0) | 1,371 (0.0) |
Detailed performance:
| Sequence | MOTA | IDF1 | HOTA | MT | ML | FP | FN | Rcll | Prcn | AssA | DetA | AssRe | AssPr | DetRe | DetPr | LocA | FAF | ID Sw. | Frag |
| ADL-Rundle-1 | 36.2 | 50.2 | 39.0 | 11 | 3 | 2,518 | 3,368 | 63.8 | 70.2 | 39.6 | 38.7 | 45.4 | 63.4 | 49.8 | 54.8 | 77.6 | 5.0 | 48 | 120 |
| ADL-Rundle-3 | 48.7 | 47.7 | 39.7 | 12 | 8 | 646 | 4,518 | 55.6 | 89.7 | 36.9 | 43.2 | 44.5 | 64.4 | 47.0 | 75.9 | 84.0 | 1.0 | 54 | 61 |
| AVG-TownCentre | 38.9 | 34.3 | 28.3 | 45 | 44 | 1,121 | 2,968 | 58.5 | 78.9 | 22.1 | 37.2 | 40.6 | 31.5 | 43.8 | 59.0 | 76.0 | 2.5 | 281 | 495 |
| ETH-Crossing | 46.4 | 46.9 | 39.0 | 5 | 10 | 74 | 458 | 54.3 | 88.0 | 35.6 | 42.8 | 51.5 | 52.2 | 46.8 | 75.9 | 84.6 | 0.3 | 6 | 11 |
| ETH-Jelmoli | 59.6 | 47.8 | 42.0 | 17 | 12 | 328 | 683 | 73.1 | 85.0 | 34.6 | 51.0 | 66.4 | 39.2 | 59.7 | 69.4 | 82.5 | 0.7 | 14 | 37 |
| ETH-Linthescher | 49.6 | 40.3 | 37.1 | 36 | 98 | 382 | 4,081 | 54.3 | 92.7 | 32.9 | 42.0 | 56.8 | 41.7 | 44.8 | 76.5 | 82.5 | 0.3 | 42 | 88 |
| KITTI-16 | 50.4 | 58.9 | 37.4 | 3 | 1 | 162 | 658 | 61.3 | 86.6 | 35.3 | 39.6 | 45.1 | 51.0 | 44.2 | 62.4 | 74.4 | 0.8 | 24 | 44 |
| KITTI-19 | 47.4 | 47.6 | 34.0 | 9 | 19 | 389 | 2,369 | 55.7 | 88.4 | 30.8 | 37.9 | 44.5 | 44.2 | 41.6 | 66.0 | 76.4 | 0.4 | 51 | 142 |
| PETS09-S2L2 | 48.1 | 31.5 | 24.0 | 5 | 4 | 500 | 4,243 | 56.0 | 91.5 | 14.5 | 40.1 | 19.6 | 33.6 | 43.2 | 70.7 | 78.4 | 1.1 | 263 | 333 |
| TUD-Crossing | 78.5 | 44.8 | 35.9 | 8 | 0 | 30 | 186 | 83.1 | 96.8 | 22.5 | 57.7 | 38.6 | 32.2 | 61.9 | 72.2 | 77.6 | 0.1 | 21 | 18 |
| Venice-1 | 49.9 | 46.1 | 36.9 | 6 | 4 | 493 | 1,779 | 61.0 | 85.0 | 33.8 | 40.6 | 41.6 | 56.6 | 46.5 | 64.7 | 78.9 | 1.1 | 16 | 22 |
Raw data:
