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Simulation of Synthetically Degraded Tracking Data to Benchmark MOT Metrics

Hartmann, M.; Löffler, Katharina ORCID iD icon 1; Mikut, Ralf ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Multiple object tracking (MOT) is an essential task in computer vision, with many practical applications in surveillance, robotics, autonomous driving, and biology. To compare different MOT algorithms efficiently and select the best
MOT algorithm for an application, we rely on tracking metrics that reduce the performance of a tracking algorithm to a single score.
However, there is a lack in testing the tracking metrics themselves, which can result in unnoticed biases or flaws in tracking metrics that can influence the decision of selecting the best tracking algorithm. To check tracking metrics for possible limitations or biases towards penalizing specific tracking errors, a standardized evaluation of tracking metrics is needed.
We propose benchmarking tracking metrics using synthetic, erroneous tracking results that simulate real-world tracking errors. First, we select common real-world tracking errors from the literature and describe how to emulate them. Then, we validate our approach by reproducing previously found tracking metric limitations through simulating specific tracking errors. In addition, our benchmark reveals a before unreported limitation in the tracking metric AOGM. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000154157
Veröffentlicht am 23.01.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Biologische und Chemische Systeme (IBCS)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator ISBN: 978-3-7315-1239-4
KITopen-ID: 1000154157
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Erschienen in Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Ed.: H. Schulte
Veranstaltung 32. Workshop Computational Intelligence (2022), Berlin, Deutschland, 01.12.2022 – 02.12.2022
Verlag KIT Scientific Publishing
Seiten 163-180
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