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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
PEDESTRIAN 46.34 % 42.03 % 51.32 % 44.51 % 71.14 % 54.45 % 73.11 % 76.87 %

Benchmark TP FP FN
PEDESTRIAN 13532 9618 953

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 53.58 % 72.82 % 54.34 % 175 37.70 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 30.24 % 44.67 % 25.09 % 762

Benchmark # Dets # Tracks
PEDESTRIAN 14485 356

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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