Method

Behavioral Multi Person Tracker [la] [on] [Be-Track]


Submitted on 15 Oct. 2018 12:12 by
Martin Dimitrievski (IPI/TELIN)

Running time:0.02 s
Environment:GPU @ 1.5 Ghz (C/C++)

Method Description:
MOT based on 2D-3D particle filter
Parameters:
Latex Bibtex:
@Article{s19020391,
AUTHOR = {Dimitrievski, Martin and Veelaert, Peter and Philips, Wilfried},
TITLE = {Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle},
JOURNAL = {Sensors},
VOLUME = {19},
YEAR = {2019},
NUMBER = {2},
ARTICLE-NUMBER = {391},
URL = {http://www.mdpi.com/1424-8220/19/2/391},
ISSN = {1424-8220},
DOI = {10.3390/s19020391}
}

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 51.29 % 72.71 % 51.80 % 91.84 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 57.38 % 91.68 % 70.59 % 13389 1215 9943 10.92 % 15412 342

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 20.96 % 47.77 % 31.27 % 118 848

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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