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 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 43.36 % 39.99 % 47.23 % 43.00 % 69.03 % 51.28 % 69.60 % 76.78 %

Benchmark TP FP FN
PEDESTRIAN 13197 9953 1225

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 50.85 % 72.45 % 51.72 % 199 35.15 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 22.34 % 45.02 % 32.65 % 731

Benchmark # Dets # Tracks
PEDESTRIAN 14422 319

This table as LaTeX