Method

Youtu Lab [youtu]
[Anonymous Submission]

Submitted on 31 Oct. 2017 13:54 by
[Anonymous Submission]

Running time:0.6 s
Environment:4 cores @ 2.5 Ghz (Python + C/C++)

Method Description:
Based on tracking by detection algorithm, prepared for
CVPR
Parameters:
detector threshold is 0.06 and tracking threshold is
0.04
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 87.76 % 84.82 % 88.41 % 87.75 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.27 % 97.23 % 94.69 % 35496 1011 2974 9.09 % 43310 1590

Benchmark MT PT ML IDS FRAG
CAR 78.77 % 18.15 % 3.08 % 223 568

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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