Benchmark:

MOT16 |

Short name:

JPDA_m_16

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.

The following parameter set was used for MOT16:
Prun_Thre=0.36562
tret=8.0504
Term_Frame=71.5978
PD=0.97546
q1=5.2186
Mcov=93.906
MF=1
m=100
Gatesq=20
FPPI=3
Upos=370.2145
Uvel=110.0264
AR=0.33
fpn=10

Reference:

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

Last submitted:

March 21, 2016 (8 years ago)

Published:

March 20, 2016 at 00:00:00 CET

Submissions:

1

Project page / code:

http://research.milanton.net/files/iccv2015/jpda_m.zip

Open source:

Yes

Hardware:

3 GHz, 1 CPU

Runtime:

22.2 Hz

Benchmark performance:

Detailed performance:

Raw data:

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