Bayesian Nonparametric Approaches to Abnormality Detection in Video Surveillance
MetadataShow full item record
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 surveillance. The important steps in identifying such events include stream data segmentation and hidden patterns discovery. However, the crucial challenge in stream data segmentation 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 Nonparametric Factor Analysis for stream data segmentation and pattern discovery. In addition, we introduce an interactive system allowing users to inspect and browse suspicious events.
Showing items related by title, author, creator and subject.
Rao, Arjun (2009)With security and surveillance gaining paramount importance in recent years, it has become important to reliably automate some surveillance tasks for monitoring crowded areas. The need to automate this process also supports ...
Rana, S.; Phung, D.; Pham, DucSon; Venkatesh, S. (2012)We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for ...
Nguyen, T.; Rana, S.; Phung, D.; Pham, DucSon; Venkatesh, S. (2012)This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified ...