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2003
IEEE

Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments

11 years 1 months ago
Recognising and Monitoring High-Level Behaviours in Complex Spatial Environments
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 spaironments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability. M 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.
Nam Thanh Nguyen, Hung Hai Bui, Svetha Venkatesh,
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2003
Where CVPR
Authors Nam Thanh Nguyen, Hung Hai Bui, Svetha Venkatesh, Geoff A. W. West
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