INARLA:
AVG-TownCentre
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.
Benchmark:
MOT15 |
Short name:
INARLA
Detector:
Public
Description:
n/a
Reference:
H. Wu, Y. Hu, K. Wang, H. Li, L. Nie, H. Cheng. Instance-aware representation learning and association for online multi-person tracking. In Pattern Recognition, 2019.
Last submitted:
February 11, 2018 (6 years ago)
Published:
August 09, 2019 at 10:02:30 CET
Submissions:
1
Project page / code:
n/a
Open source:
No
Hardware:
CPU: 8 core, 3.7 GHz, GPU: Tesla K40
Runtime:
2.6 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 | 34.7 | 42.1 | 31.8 | 90 (12.5) | 216 (30.0) | 9,855 | 29,158 | 52.5 | 76.6 | 29.5 | 34.5 | 33.0 | 63.3 | 40.0 | 58.3 | 74.8 | 1.7 | 1,112 (21.2) | 2,848 (54.2) |
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 | 20.1 | 42.1 | 31.8 | 6 | 3 | 3,463 | 3,868 | 58.4 | 61.1 | 30.8 | 32.9 | 34.1 | 62.3 | 44.5 | 46.5 | 73.5 | 6.9 | 100 | 402 |
| ADL-Rundle-3 | 36.0 | 37.8 | 29.7 | 7 | 8 | 1,383 | 5,014 | 50.7 | 78.8 | 25.1 | 35.3 | 27.7 | 65.1 | 40.3 | 62.7 | 77.5 | 2.2 | 109 | 221 |
| AVG-TownCentre | 32.1 | 45.7 | 32.4 | 20 | 54 | 955 | 3,649 | 49.0 | 78.6 | 32.4 | 33.0 | 36.4 | 61.9 | 36.7 | 58.8 | 72.4 | 2.1 | 250 | 570 |
| ETH-Crossing | 35.2 | 48.5 | 33.1 | 2 | 11 | 72 | 566 | 43.6 | 85.9 | 34.3 | 32.1 | 36.8 | 72.3 | 34.6 | 68.1 | 78.1 | 0.3 | 12 | 22 |
| ETH-Jelmoli | 48.1 | 57.1 | 40.9 | 13 | 13 | 329 | 955 | 62.4 | 82.8 | 40.9 | 40.9 | 48.7 | 62.7 | 47.4 | 63.0 | 77.8 | 0.7 | 33 | 76 |
| ETH-Linthescher | 41.3 | 48.3 | 35.7 | 20 | 104 | 472 | 4,704 | 47.3 | 90.0 | 37.3 | 34.2 | 41.0 | 69.0 | 36.5 | 69.3 | 77.2 | 0.4 | 69 | 164 |
| KITTI-16 | 32.9 | 55.0 | 36.7 | 0 | 1 | 329 | 786 | 53.8 | 73.6 | 38.8 | 34.7 | 42.6 | 60.1 | 40.1 | 54.9 | 71.7 | 1.6 | 26 | 113 |
| KITTI-19 | 24.4 | 46.9 | 33.3 | 7 | 15 | 1,324 | 2,649 | 50.4 | 67.0 | 33.7 | 33.1 | 37.7 | 60.0 | 39.3 | 52.3 | 71.5 | 1.3 | 67 | 326 |
| PETS09-S2L2 | 40.5 | 26.1 | 20.5 | 3 | 5 | 703 | 4,657 | 51.7 | 87.6 | 12.0 | 35.2 | 13.7 | 55.2 | 38.1 | 64.6 | 74.4 | 1.6 | 380 | 743 |
| TUD-Crossing | 73.0 | 63.3 | 45.8 | 7 | 0 | 12 | 260 | 76.4 | 98.6 | 39.3 | 53.4 | 43.0 | 66.7 | 56.5 | 72.9 | 77.8 | 0.1 | 25 | 51 |
| Venice-1 | 36.4 | 40.1 | 31.3 | 5 | 2 | 813 | 2,050 | 55.1 | 75.6 | 29.1 | 33.9 | 33.7 | 62.5 | 40.2 | 55.2 | 74.3 | 1.8 | 41 | 160 |
Raw data:
