Method

ST-3D [st] [ST-3D]
[Anonymous Submission]

Submitted on 22 Nov. 2019 03:47 by
[Anonymous Submission]

Running time:0.07 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TDA
Parameters:
TDA
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 82.64 % 83.83 % 83.72 % 87.12 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 85.60 % 99.27 % 91.93 % 31905 234 5366 2.10 % 36013 1908

Benchmark MT PT ML IDS FRAG
CAR 61.69 % 31.08 % 7.23 % 370 856

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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