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Failure-State Assessment and Transition Estimation for Tracking-by-Detection Trackers under Composite Detection-Output Perturbations

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(10), pp.29~35
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 4, 2026
  • Accepted : September 30, 2026
  • Published : October 30, 2026

Young-Jun Oh 1

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Accredited

ABSTRACT

In this paper, the Failure-State Assessment and Transition Estimation Scheme(FATES) for Tracking-by-Detection is proposed. FATES transforms degradation in MOT metrics into multi-label failure compositions and perturbation-space transition intervals using four labels: recall loss, fragmentation, identity-association failure, and localization instability. Rules selected from development data are evaluated against predefined operational event references using 1,260 synthetic runs and 45 window–tracker samples from MOT17, MOT20, and DanceTrack. On synthetic data, agreement with these references yielded 96.8% Macro-F1, 96.9% Micro-F1, and 91.1% exact match. External evaluation was limited to supported failure states and did not assess transitions. An offline application improved mean IDF1 from 64.0% to 67.8% and MOTA from 53.9% to 69.9% on 630 synthetic outputs. On 45 external samples, the gains were limited: IDF1 increased from 55.4% to 55.6% and MOTA from 50.9% to 51.3%.

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