Method

Hungarian Algorithm [on] [HM]


Submitted on 25 Sep. 2013 18:10 by
Philip Lenz (KIT)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Frame-by-frame data association using the Hungarian
Algorithm. Similarity is measured using bounding
box overlap and RGB histogram similarity.
Parameters:
None
Latex Bibtex:
@PHDTHESIS{Geiger2013,
author = {Andreas Geiger},
title = {Probabilistic Models for 3D Urban Scene
Understanding from Movable Platforms},
school = {KIT},
year = {2013}
}

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 43.85 % 78.34 % 43.88 % 85.36 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 45.03 % 99.67 % 62.04 % 15769 53 19247 0.48 % 16532 1102

Benchmark MT PT ML IDS FRAG
CAR 12.46 % 48.00 % 39.54 % 12 571

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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