Bayesian Inference Application to Burglary Detection

Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [6...

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Bibliographic Details
Main Author:	Bhale, Ishan Singh
Other Authors:	Mukhopadhyay, Suprathik
Format:	Others
Language:	en
Published:	LSU 2013
Subjects:	Computer Science
Online Access:	http://etd.lsu.edu/docs/available/etd-01222013-155000/

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spelling	ndltd-LSU-oai-etd.lsu.edu-etd-01222013-1550002013-01-25T03:09:12Z Bayesian Inference Application to Burglary Detection Bhale, Ishan Singh Computer Science Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [61]. These complex activities are usually a combination of various articulated activities like breaking windshield, digging, and non-articulated activities like walking, running. We have developed a Bayesian framework for recognizing complex activities like burglary. This framework (belief network) is based on an expectation propagation algorithm [8] for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time. Mukhopadhyay, Suprathik Busch, Konstantin Chen, Jianhua Brener, Nathan LSU 2013-01-24 text application/pdf http://etd.lsu.edu/docs/available/etd-01222013-155000/ http://etd.lsu.edu/docs/available/etd-01222013-155000/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.
collection	NDLTD
language	en
format	Others
sources	NDLTD
topic	Computer Science
spellingShingle	Computer Science Bhale, Ishan Singh Bayesian Inference Application to Burglary Detection
description	Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [61]. These complex activities are usually a combination of various articulated activities like breaking windshield, digging, and non-articulated activities like walking, running. We have developed a Bayesian framework for recognizing complex activities like burglary. This framework (belief network) is based on an expectation propagation algorithm [8] for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time.
author2	Mukhopadhyay, Suprathik
author_facet	Mukhopadhyay, Suprathik Bhale, Ishan Singh
author	Bhale, Ishan Singh
author_sort	Bhale, Ishan Singh
title	Bayesian Inference Application to Burglary Detection
title_short	Bayesian Inference Application to Burglary Detection
title_full	Bayesian Inference Application to Burglary Detection
title_fullStr	Bayesian Inference Application to Burglary Detection
title_full_unstemmed	Bayesian Inference Application to Burglary Detection
title_sort	bayesian inference application to burglary detection
publisher	LSU
publishDate	2013
url	http://etd.lsu.edu/docs/available/etd-01222013-155000/
work_keys_str_mv	AT bhaleishansingh bayesianinferenceapplicationtoburglarydetection
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Bayesian Inference Application to Burglary Detection

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