Method

NOTA_HWDPL [NOTA]
[Anonymous Submission]

Submitted on 6 Sep. 2018 15:27 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
Near-Online Tracklet Association
Parameters:
private
Latex Bibtex:
@inproceedings{wang2019multiple,
title={BMVC 2019 Submission}
}

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 57.67 % 72.17 % 58.14 % 91.27 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 65.51 % 90.43 % 75.98 % 15325 1621 8070 14.57 % 19569 541

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 34.36 % 46.39 % 19.24 % 108 799

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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