Method

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


Submitted on 26 Nov. 2014 11:27 by
Chang-Ryeol Lee (Gwangju Institue of Science and Technology)

Running time:0.01 s
Environment:1 core @ 3.5 Ghz (Matlab)

Method Description:
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 52.42 % 75.18 % 52.56 % 81.20 %
PEDESTRIAN 34.54 % 68.06 % 34.89 % 91.84 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 57.72 % 93.72 % 71.44 % 20408 1367 14947 12.29 % 23677 680
PEDESTRIAN 43.94 % 83.16 % 57.49 % 10194 2065 13008 18.56 % 14359 296

Benchmark MT PT ML IDS FRAG
CAR 21.69 % 46.46 % 31.85 % 50 376
PEDESTRIAN 14.43 % 38.14 % 47.42 % 81 685

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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