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HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

International Journal of Computer Vision · 2020 · Vol. 129(2) · pp. 548–578
Jonathon LuitenAljos̆a Os̆epPatrick DendorferPhilip TorrAndreas GeigerLaura Leal-TaixéBastian Leibe

Abstract

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.

Video Surveillance and Tracking MethodsGaze Tracking and Assistive TechnologyTarget Tracking and Data Fusion in Sensor NetworksMetric (unit)Tracking (education)Data associationPattern recognition (psychology)Tracking errorAssociation (psychology)Video tracking

Funding

  • Alexander von Humboldt-Stiftung
  • Engineering and Physical Sciences Research Council
Citations
951
FWCI
30.03
field-weighted impact
References
79
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100%
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References
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision · 2009 · 19,127 citations
ImageNet Large Scale Visual Recognition Challenge
International Journal of Computer Vision · 2015 · 39,683 citations
An algorithm for tracking multiple targets
IEEE Transactions on Automatic Control · 1979 · 3,016 citations
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