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Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
Author(s) -
Keni Bernardin,
Rainer Stiefelhagen
Publication year - 2008
Publication title -
eurasip journal on image and video processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 40
eISSN - 1687-5281
pISSN - 1687-5176
DOI - 10.1155/2008/246309
Subject(s) - biometrics , computer science , artificial intelligence , computer vision , video tracking , object (grammar) , pattern recognition (psychology)
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 label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations

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