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 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 33.79 % 34.30 % 33.45 % 35.16 % 79.56 % 34.55 % 83.08 % 81.33 %

Benchmark TP FP FN
CAR 15145 19247 53

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 42.36 % 78.40 % 43.88 % 523 32.85 %

Benchmark MT rate PT rate ML rate FRAG
CAR 12.62 % 47.23 % 40.15 % 690

Benchmark # Dets # Tracks
CAR 15198 960

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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