Master of Science in Computer Science (MSCS)
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 . Surveillance videos can contain high level plans in the form of complex activities . 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  for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time.
Document Availability at the Time of Submission
Release the entire work immediately for access worldwide.
Bhale, Ishan Singh, "Bayesian inference application to burglary detection" (2013). LSU Master's Theses. 2382.