Method

Network flow with jointly trained cost functions [SLP*]
[Anonymous Submission]

Submitted on 15 Nov. 2016 17:38 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
* Using Regionlet detections
Parameters:
n/a
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 75.79 % 78.79 % 75.96 % 83.26 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 81.39 % 96.49 % 88.30 % 31196 1135 7133 10.20 % 35182 1108

Benchmark MT PT ML IDS FRAG
CAR 53.85 % 36.62 % 9.54 % 59 543

This table as LaTeX


[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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