Benchmark:
Short name:
MOANA
Detector:
Public
Description:
(1) 3D Kalman-filter-based tracking for fully visible object(s)
(2) Cross-matching based on adaptive appearance modeling for partially occluded/grouped object(s)
(3) Re-identification based on adaptive appearance modeling for seriously occluded object(s)
Reference:
Z. Tang, J. Hwang. MOANA: An online learned adaptive appearance model for robust multiple object tracking in 3D. In IEEE Access, 2019.
Last submitted:
December 06, 2017 (6 years ago)
Published:
September 04, 2018 at 01:28:28 CET
Submissions:
3
Project page / code:
n/a
Open source:
No
Hardware:
4.20 GHz, 4 Cores
Runtime:
19.4 Hz
Benchmark performance:
Sequence | MOTA | IDF1 | HOTA | MT | ML | FP | FN | Rcll | Prcn | AssA | DetA | AssRe | AssPr | DetRe | DetPr | LocA | FAF | ID Sw. | Frag |
3D MOT 2015 | 52.7 | 62.4 | 0.0 | 76 (28.4) | 59 (22.0) | 2,226 | 5,551 | 66.9 | 83.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.5 | 167 (2.5) | 586 (8.8) |
Detailed performance:
Sequence | MOTA | IDF1 | HOTA | MT | ML | FP | FN | Rcll | Prcn | AssA | DetA | AssRe | AssPr | DetRe | DetPr | LocA | FAF | ID Sw. | Frag |
AVG-TownCentre | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
PETS09-S2L2 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
Raw data: