Method

[on][st] [3D-TLSR ]
[Anonymous Submission]

Submitted on 15 Jul. 2019 22:35 by
[Anonymous Submission]

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

Method Description:
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Parameters:
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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
PEDESTRIAN 54.00 % 73.03 % 54.44 % 91.59 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 58.90 % 93.60 % 72.30 % 13767 942 9606 8.47 % 15373 388

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 29.55 % 46.74 % 23.71 % 100 835

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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