RMOT: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects

TUD-Crossing


Benchmark:

Short name:

RMOT

Detector:

Public

Description:

Reference:

J. Yoon, H. Yang, J. Lim, K. Yoon. Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015.

Last submitted:

December 09, 2014 (5 years ago)

Published:

December 09, 2014 at 03:08:21 CET

Submissions:

2

Project page / code:

n/a

Open source:

No

Hardware:

3.5 GHz, 1 Core

Runtime:

7.9 Hz

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
2D MOT 201518.632.669.638 (5.3)384 (53.3)12,47336,83540.066.42.2684 (17.1)1,282 (32.0)

Detailed performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
ADL-Rundle-10.00.00.000000.00.00.000
ADL-Rundle-30.00.00.000000.00.00.000
AVG-TownCentre0.00.00.000000.00.00.000
ETH-Crossing0.00.00.000000.00.00.000
ETH-Jelmoli0.00.00.000000.00.00.000
ETH-Linthescher0.00.00.000000.00.00.000
KITTI-160.00.00.000000.00.00.000
KITTI-190.00.00.000000.00.00.000
PETS09-S2L20.00.00.000000.00.00.000
TUD-Crossing0.00.00.000000.00.00.000
Venice-10.00.00.000000.00.00.000

Raw data: