You are not logged in.
Openly accessible

Recognising and monitoring high-level behaviours in complex spatial environments

Nguyen, Nam T., Bui, Hung H., Venkatesh, Svetha and West, Geoff 2003, Recognising and monitoring high-level behaviours in complex spatial environments, in CVPR 2003 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 620-625, doi: 10.1109/CVPR.2003.1211524.

Attached Files
Name Description MIMEType Size Downloads
venkatesh-recognisingand-2003.pdf Published version application/pdf 328.11KB 169

Title Recognising and monitoring high-level behaviours in complex spatial environments
Alternative title Recognizing and monitoring high-level behaviours in complex spatial environments
Author(s) Nguyen, Nam T.
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff
Conference name Conference on Computer Vision and Pattern Recognition (2003 : Madison, Wis.)
Conference location Madison, Wis.
Conference dates 18 -20 Jun. 2003
Title of proceedings CVPR 2003 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2003
Conference series Computer Vision and Pattern Recognition. Conference
Start page 620
End page 625
Total pages 6
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) hidden Markov models
image motion analysis
object detection
surveillance
video cameras
Summary The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769519005
0769519008
ISSN 1063-6919
Language eng
DOI 10.1109/CVPR.2003.1211524
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2003, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044640

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 68 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 237 Abstract Views, 169 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 11:36:57 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.