Method

wait a name [on] [wan]
[Anonymous Submission]

Submitted on 13 Nov. 2016 00:11 by
[Anonymous Submission]

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

Method Description:
based on scea
Parameters:
same as scea
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 78.07 % 82.83 % 78.14 % 86.92 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 80.28 % 99.49 % 88.85 % 29962 155 7362 1.39 % 31817 782

Benchmark MT PT ML IDS FRAG
CAR 51.38 % 35.23 % 13.38 % 24 235

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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