Bayesian nonparametric approaches to abnormality detection in video surveillance

Nguyen, Vu, Phung, Dinh, Pham, Duc-Son and Venkatesh, Svetha 2015, Bayesian nonparametric approaches to abnormality detection in video surveillance, Annals of data science, vol. 2, no. 1, pp. 21-41, doi: 10.1007/s40745-015-0030-3.

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Title Bayesian nonparametric approaches to abnormality detection in video surveillance
Author(s) Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh
Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name Annals of data science
Volume number 2
Issue number 1
Start page 21
End page 41
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-03
ISSN 2198-5804
Keyword(s) abnormal detection
bayesian nonparametric
user interface
multilevel data structure
video segmentation
spatio-temporal browsing
Summary In data science, anomaly detection is the process of identifying the items, events or observations which do not conform to expected patterns in a dataset. As widely acknowledged in the computer vision community and security management, discovering suspicious events is the key issue for abnormal detection in video surveil-lance. The important steps in identifying such events include stream data segmentation and hidden patterns discovery. However, the crucial challenge in stream data segmenta-tion and hidden patterns discovery are the number of coherent segments in surveillance stream and the number of traffic patterns are unknown and hard to specify. Therefore, in this paper we revisit the abnormality detection problem through the lens of Bayesian nonparametric (BNP) and develop a novel usage of BNP methods for this problem. In particular, we employ the Infinite Hidden Markov Model and Bayesian Nonparamet-ric Factor Analysis for stream data segmentation and pattern discovery. In addition, we introduce an interactive system allowing users to inspect and browse suspicious events.
Language eng
DOI 10.1007/s40745-015-0030-3
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer
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