Anomaly detection in video with Bayesian nonparametrics
Machine Learning
2016-06-29 v1
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.
Keywords
Cite
@article{arxiv.1606.08455,
title = {Anomaly detection in video with Bayesian nonparametrics},
author = {Olga Isupova and Danil Kuzin and Lyudmila Mihaylova},
journal= {arXiv preprint arXiv:1606.08455},
year = {2016}
}
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5 pages