TUD-Crossing
Benchmark:
Short name:
SegTrack
Detector:
Public
Description:
We propose a multi-target tracker that exploits low level image
information and associates every (super)-pixel to a specific target or
classifies it as background. As a result, we obtain a video
segmentation in addition to the classical bounding-box representation in
unconstrained, real-world videos.
Reference:
A. Milan, L. Leal-Taixé, K. Schindler, I. Reid. Joint Tracking and Segmentation of Multiple Targets. In CVPR, 2015.
Last submitted:
April 10, 2015 (5 years ago)
Published:
April 10, 2015 at 11:33:33 CET
Submissions:
3
Project page / code:
Open source:
Yes
Hardware:
2.7 GHz, 1 Core
Runtime:
0.2 Hz
Benchmark performance:
Sequence | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag |
2D MOT 2015 | 22.5 | 31.5 | 71.7 | 42 (5.8) | 461 (63.9) | 7,890 | 39,020 | 36.5 | 74.0 | 1.4 | 697 (19.1) | 737 (20.2) |
Detailed performance:
Sequence | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag |
ADL-Rundle-1 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
ADL-Rundle-3 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
AVG-TownCentre | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
ETH-Crossing | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
ETH-Jelmoli | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
ETH-Linthescher | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
KITTI-16 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
KITTI-19 | 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 |
TUD-Crossing | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
Venice-1 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
Raw data: