Method

Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects [on] [RMOT] [RMOT*]


Submitted on 4 Dec. 2014 08:32 by
Chang-Ryeol Lee (Gwangju Institue of Science and Technology)

Running time:0.02 s
Environment:1 core @ 3.5 Ghz (Matlab)

Method Description:
* use Regionlet detetions available at:
http://www.cvlibs.net/datasets/kitti/eval_tracking

.php
Parameters:
Latex Bibtex:
@inproceedings{
Yoon2015WACV,
author = "Ju Hong Yoon and Ming-Hsuan Yang and
Jongwoo
Lim and Kuk-Jin Yoon",
booktitle = "IEEE Winter Conference on
Applications
of
Computer Vision (WACV)",
title = "Bayesian Multi-Object Tracking Using
Motion
Context from Multiple Objects",
year = "2015"
}

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 65.83 % 75.42 % 66.43 % 80.76 %
PEDESTRIAN 43.77 % 71.02 % 44.43 % 92.38 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 80.58 % 88.09 % 84.17 % 30689 4148 7396 37.29 % 39256 940
PEDESTRIAN 53.64 % 85.75 % 66.00 % 12484 2075 10790 18.65 % 16262 298

Benchmark MT PT ML IDS FRAG
CAR 40.15 % 50.15 % 9.69 % 209 727
PEDESTRIAN 19.59 % 39.18 % 41.24 % 153 748

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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