Active online anomaly detection using dirichlet process mixture model and Gaussian process classification

Jagannadan Varadarajan, Ramanathan Subramanian, Narendra Ahuja, Pierre Moulin, Jean Marc Odobez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel anomaly detection (AD) system for streaming videos. Different from prior methods that rely on unsupervised learning of clip representations, that are usually coarse in nature, and batch-mode learning, we propose the combination of two non-parametric models for our task: i) Dirichlet process mixture models (DPMM) based modeling of object motion and directions in each cell, and ii) Gaussian process based active learning paradigm involving labeling by a domain expert. Whereas conventional clip representation methods adopt quantizing only motion directions leading to a lossy, coarse representation that are inadequate, our clip representation approach results in fine grained clusters at each cell that model the scene activities (both direction and speed) more effectively. For active anomaly detection, we adapt a Gaussian Process framework to process incoming samples (video snippets) sequentially, seek labels for confusing or informative samples and and update the AD model online. Furthermore, the proposed video representation along with a novel query criterion to select informative samples for labeling that incorporates both exploration and exploitation criteria is proposed, and is found to outperform competing criteria on two challenging traffic scene datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages615-623
Number of pages9
ISBN (Electronic)9781509048229
DOIs
StatePublished - May 11 2017
Event17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017 - Santa Rosa, United States
Duration: Mar 24 2017Mar 31 2017

Publication series

NameProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017

Other

Other17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
CountryUnited States
CitySanta Rosa
Period3/24/173/31/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Varadarajan, J., Subramanian, R., Ahuja, N., Moulin, P., & Odobez, J. M. (2017). Active online anomaly detection using dirichlet process mixture model and Gaussian process classification. In Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017 (pp. 615-623). [7926657] (Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2017.74