JPDA_m: Joint Probabilistic Data Association Revisited

TUD-Crossing


Benchmark:

Short name:

JPDA_m

Detector:

Public

Description:

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.

Reference:

H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015.

Last submitted:

April 14, 2015 (5 years ago)

Published:

April 22, 2015 at 09:02:53 CET

Submissions:

2

Open source:

No

Hardware:

3 GHz, 1 Core

Runtime:

32.6 Hz

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
2D MOT 201523.833.868.236 (5.0)419 (58.1)6,37340,08434.877.01.1365 (10.5)869 (25.0)

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: