Identifier
etd-01222013-155000
Degree
Master of Science in Computer Science (MSCS)
Department
Computer Science
Document Type
Thesis
Abstract
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.
Date
2013
Document Availability at the Time of Submission
Release the entire work immediately for access worldwide.
Recommended Citation
Bhale, Ishan Singh, "Bayesian inference application to burglary detection" (2013). LSU Master's Theses. 2382.
https://repository.lsu.edu/gradschool_theses/2382
Committee Chair
Mukhopadhyay, Suprathik
DOI
10.31390/gradschool_theses.2382