Method

Discrete-Continuous Optimization [DCO]
http://research.milanton.net/dctracking/

Submitted on 21 May. 2014 10:21 by
Anton Milan (University of Adelaide)

Running time:0.03 s
Environment:1 core @ >3.5 Ghz (Matlab + C/C++)

Method Description:
A global multi-target tracking approach
that jointly addresses data association and
trajectory estimation by minimizing a consistent
discrete-continuous energy.
Parameters:
Default parameters with provided L-SVM detections
(threshold 0.0)
Latex Bibtex:
@inproceedings{Andriyenko2012CVPR,
Author = {Anton Andriyenko and Konrad
Schindler and Stefan Roth},
Booktitle = {CVPR},
Title = {Discrete-Continuous Optimization
for Multi-Target Tracking},
Year = {2012}
}

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 33.45 % 36.33 % 31.30 % 40.93 % 64.11 % 34.23 % 73.46 % 77.25 %

Benchmark TP FP FN
CAR 17501 16891 4458

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 36.72 % 74.34 % 37.92 % 414 23.66 %

Benchmark MT rate PT rate ML rate FRAG
CAR 15.54 % 53.38 % 31.08 % 405

Benchmark # Dets # Tracks
CAR 21959 760

This table as LaTeX

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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