Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

Karlsruhe Institute of Technology

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Abstract

Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently…

Citation impact

2,820
total citations
FWCI
17.39
Percentile
100%
References
13
Citations per year

Authors

2

Topics & keywords

Keywords
  • Biometrics
  • Computer science
  • Artificial intelligence
  • Computer vision
  • Video tracking
  • Object (grammar)
  • Pattern recognition (psychology)
UN Sustainable Development Goals
  • Partnerships for the goals
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