Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2 p. 620
Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments
Nam T. Nguyen, Curtin University of Technology
Hung H. Bui, Curtin University of Technology
Svetha Venkatesh, Curtin University of Technology
Geoff West, Curtin University of Technology

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211524
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Abstract
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.
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Citation: Nam T. Nguyen, Hung H. Bui, Svetha Venkatesh, Geoff West, "Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments," cvpr, p. 620, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003

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