Method

VV_team [on][la] [VV_team]


Submitted on 4 Dec. 2019 09:23 by
_ _ (_)

Running time:0.1 s
Environment:GPU @ 3.0 Ghz (C/C++)

Method Description:
_
Parameters:
_
Latex Bibtex:
_

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
CAR 86.10 % 84.83 % 87.38 % 87.85 %
PEDESTRIAN 55.40 % 72.29 % 56.27 % 91.01 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.53 % 99.24 % 94.14 % 34842 268 4073 2.41 % 41255 1957
PEDESTRIAN 66.85 % 86.86 % 75.55 % 15640 2366 7757 21.27 % 22281 893

Benchmark MT PT ML IDS FRAG
CAR 76.77 % 19.69 % 3.54 % 439 859
PEDESTRIAN 36.77 % 49.14 % 14.09 % 201 1131

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.


eXTReMe Tracker