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: A Conference proceeding or a Chapter in BookConference contributionpeer-review

5 Citations (Scopus)

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
Title of host publicationProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
EditorsGerard Medioni, David Michael, Conrad Sanderson, Matthew Turk
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages615-623
Number of pages9
ISBN (Electronic)9781509048229
ISBN (Print)9781509048236
DOIs
Publication statusPublished - 11 May 2017
Externally publishedYes
Event17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017 - Santa Rosa, United States
Duration: 24 Mar 201731 Mar 2017

Publication series

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

Conference

Conference17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
CountryUnited States
CitySanta Rosa
Period24/03/1731/03/17

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