HAM_INTP15: Historical Appearance Matching and Scene Adaptive Detection Filtering (VisBest)
ETH-Linthescher
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.
Benchmark:
MOT15 |
Short name:
HAM_INTP15
Detector:
Public
Description:
https://github.com/yyc9268/Deep-TAMA
Reference:
Y. Yoon, A. Boragule, Y. Song, K. Yoon, M. Jeon. Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering. In IEEE AVSS, 2018.
Last submitted:
July 15, 2018 (6 years ago)
Published:
July 15, 2018 at 18:08:48 CET
Submissions:
1
Project page / code:
Open source:
Yes
Hardware:
3.7 GHZ, 1 Core, TITAN X
Runtime:
18.7 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 | 28.6 | 41.4 | 30.4 | 72 (10.0) | 317 (44.0) | 7,485 | 35,910 | 41.6 | 77.3 | 33.0 | 28.3 | 36.2 | 69.0 | 31.8 | 59.1 | 75.2 | 1.3 | 460 (11.1) | 1,038 (25.0) |
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 | 16.7 | 38.3 | 28.9 | 5 | 13 | 1,949 | 5,770 | 38.0 | 64.5 | 35.0 | 24.2 | 37.9 | 72.2 | 29.4 | 49.8 | 76.3 | 3.9 | 32 | 78 |
| ADL-Rundle-3 | 35.5 | 39.3 | 28.1 | 3 | 15 | 442 | 6,071 | 40.3 | 90.3 | 27.4 | 29.0 | 29.5 | 71.7 | 30.7 | 68.7 | 77.2 | 0.7 | 39 | 56 |
| AVG-TownCentre | 20.9 | 46.5 | 34.3 | 30 | 74 | 1,762 | 3,749 | 47.6 | 65.9 | 38.5 | 30.9 | 44.2 | 62.8 | 37.3 | 51.7 | 72.7 | 3.9 | 145 | 301 |
| ETH-Crossing | 29.0 | 46.3 | 31.0 | 2 | 17 | 28 | 681 | 32.1 | 92.0 | 40.6 | 23.7 | 44.4 | 73.9 | 24.7 | 70.8 | 78.1 | 0.1 | 3 | 3 |
| ETH-Jelmoli | 40.1 | 60.1 | 39.4 | 9 | 15 | 269 | 1,240 | 51.1 | 82.8 | 43.7 | 35.7 | 49.2 | 68.8 | 39.8 | 64.4 | 77.3 | 0.6 | 10 | 33 |
| ETH-Linthescher | 19.8 | 30.6 | 25.0 | 9 | 151 | 189 | 6,959 | 22.1 | 91.3 | 37.9 | 16.6 | 41.3 | 73.1 | 17.1 | 70.6 | 77.3 | 0.2 | 17 | 45 |
| KITTI-16 | 39.5 | 51.6 | 31.7 | 0 | 3 | 103 | 906 | 46.7 | 88.5 | 30.7 | 32.8 | 33.3 | 64.8 | 35.4 | 67.1 | 77.1 | 0.5 | 20 | 42 |
| KITTI-19 | 20.7 | 43.6 | 31.3 | 4 | 17 | 1,311 | 2,879 | 46.1 | 65.3 | 33.9 | 29.5 | 38.2 | 63.1 | 35.5 | 50.2 | 71.6 | 1.2 | 47 | 193 |
| PETS09-S2L2 | 46.0 | 40.9 | 29.4 | 5 | 3 | 704 | 4,385 | 54.5 | 88.2 | 23.2 | 37.7 | 25.1 | 64.8 | 40.4 | 65.4 | 73.9 | 1.6 | 121 | 229 |
| TUD-Crossing | 63.4 | 66.5 | 47.0 | 4 | 3 | 28 | 370 | 66.4 | 96.3 | 47.8 | 46.3 | 50.3 | 72.6 | 49.6 | 71.9 | 78.2 | 0.1 | 5 | 11 |
| Venice-1 | 20.6 | 36.8 | 28.2 | 1 | 6 | 700 | 2,900 | 36.4 | 70.4 | 33.1 | 24.2 | 34.7 | 73.6 | 28.1 | 54.2 | 76.2 | 1.6 | 21 | 47 |
Raw data:
