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 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
CAR 55.82 % 54.95 % 57.34 % 62.56 % 69.08 % 62.58 % 74.77 % 78.82 %
PEDESTRIAN 39.56 % 36.07 % 43.63 % 39.74 % 63.97 % 49.54 % 62.82 % 75.35 %

Benchmark TP FP FN
CAR 26969 7423 4175
PEDESTRIAN 12328 10822 2053

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 65.07 % 75.80 % 66.28 % 414 46.09 %
PEDESTRIAN 43.32 % 70.74 % 44.38 % 247 27.74 %

Benchmark MT rate PT rate ML rate FRAG
CAR 39.85 % 50.00 % 10.15 % 548
PEDESTRIAN 20.27 % 38.14 % 41.58 % 577

Benchmark # Dets # Tracks
CAR 31144 838
PEDESTRIAN 14381 274

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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