NDLI: The detection of unusual events in video based on Bayesian surprise model

Content Provider	IEEE Xplore Digital Library
Author	Jinsheng Xie Li Guo Yunbi Chen Long Zhao
Copyright Year	2010
Description	Author affiliation: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China (Jinsheng Xie; Li Guo; Yunbi Chen; Long Zhao)
Abstract	Automatic detection of unusual events in video surveillance has proven to be a difficult task. It is shown that surprise is an important cue for the direction of human attention to unexpected events. Aiming to the difficulty of the definition and description of abnormity in videos, we present the use of Bayesian surprise, introduced in computer vision, for detection of certain types of unusual events. Multiple local monitors which collect low-level statistics (velocity or direction) are placed over the image. With the monitors' observation and a set of reference measurement previously acquired, a pixel-wise surprise trigger is computed using Bayesian probabilistic inference techniques. Once the surprise value exceed by a preset threshold, the monitor outputs an alert. These alerts are integrated to a final decision regarding the existence of an unusual event. It was tested on a variety of real-life crowded scenes. The results show that the proposed method localized the regions of anomalies in the abnormal frames successfully, thus demonstrating the practical applicability of the scheme.
Starting Page	4784
Ending Page	4787
File Size	784013
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5691590
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Automation Bayesian methods Conferences Humans Model of Bottom-Up Saliency-Based Visual Attention Bayesian Theory of Surprise Video surveillance Computer Vision Biomedical monitoring Monitoring
Content Type	Text
Resource Type	Article

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