Global Abnormal Behaviour Detection Using a Network of CCTV Cameras
Speaker: Emanual Zelniker, Mary University of London
When: 2008-11-20 15:00:00
Venue: 78-420
Host: Vaughan Clarkson
Abstract:This paper investigates the detection of global abnormal behaviours across a network of CCTV cameras.
Although the problem of multiple camera tracking has attracted much attention recently, little work has
been done on modelling global behaviours of objects monitored by a network of CCTV cameras with disjointed
camera views, and no effort has been taken to tackle the challenging problem of detecting abnormal
global behaviours, which are only meaningful and recognisable when observed over space and time across
multiple camera views. To that end, we propose a novel framework, which consists of object tracking
across camera views, global behaviour modelling based on unsupervised learning, and probabilistic
abnormality inference. The effectiveness of the framework is demonstrated with experiments on
real-world surveillance video data.
Biography:Emanuel E. Zelniker was born in Beer-Sheva, Israel, in 1979.
He received the B.E. degree with first-class honors in electrical engineering from
the University of Queensland, Brisbane, Australia, in 2001, and the Ph.D.
degree in electrical engineering from the University of Queensland, Brisbane, Australia, in 2006.
From 2006 to 2007, he was a Postdoctoral Research Fellow with the University of Adelaide, Adelaide,
Australia, in the Australian Centre for Visual Technologies, School of Computer Science, working on
statistical tracking using a network of cameras for surveillance applications. Work was done in
collaboration with Canon Australia(CISRA) and Canon Inc. Tokyo research wings.
Since 2007, he has been a Postdoctoral Research Assistant at Queen Mary, University of London, London,
United Kingdom, in the School of Electrical Engineering and Computer Science,
working on behaviour based enhancement of wide-area situational awareness in a distributed
network of CCTV cameras. His research interests include statistical signal processing,
signal and image processing, detection and estimation, and machine learning.
Type: ITEE Seminar
Contact:Vaughan Clarkson, seminar host (.clarkson@uq.edu.au)
or Heng Tao SHEN (ITEE seminar co-ordinator)
(shenht@itee.uq.edu.au)