Method

M^3 tracker [M^3 tracker]
[Anonymous Submission]

Submitted on 7 Jul. 2020 18:38 by
[Anonymous Submission]

Running time:0.02 s
Environment:8 cores @ 3.5 Ghz (C/C++)

Method Description:
N/A
Parameters:
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Latex Bibtex:
N/A

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 79.93 % 84.77 % 80.74 % 88.83 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 84.20 % 97.93 % 90.55 % 31727 671 5952 6.03 % 36310 2036

Benchmark MT PT ML IDS FRAG
CAR 66.00 % 24.00 % 10.00 % 278 716

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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