Anomaly detection in video with Bayesian nonparametrics

Olga Isupova, Danil Kuzin, Lyudmila S. Mihaylova

Research output: Working paper / PreprintPreprint

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Abstract

A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison with a non-dynamic model shows the superiority of the proposed dynamic one in terms of the classification performance for anomaly detection.
Original languageEnglish
Publication statusPublished - 27 Jun 2016

Bibliographical note

Presented at ICML Anomaly Detection Workshop 2016

Keywords

  • stat.ML

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