Method

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


Submitted on 11 Feb. 2020 16:36 by
uyen Nguyen ( Institut für Photogrammetrie und GeoInformation, Leibniz Universtät Hannover)

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

Method Description:
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Parameters:
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Latex Bibtex:
@article{nguyen20203d,
title={3D Pedestrian tracking using local
structure constraints},
author={Nguyen, Uyen and Heipke, Christian},
journal={ISPRS Journal of Photogrammetry and
Remote Sensing},
volume={166},
pages={347--358},
year={2020},
publisher={Elsevier}
}

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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