This paper provides new insights into robust human tracking and semantic event detection within the context of a novel real-time video surveillance system capable of automatically detecting drowning incidents in a swimming pool. An effective background model that incorporates prior knowledge about swimming pools and aquatic environments enables swimmers to be reliably detected and tracked despite the significant presence of water ripples, splashes and shadows. Visual indicators of water crises are identified based on professional knowledge of water crisis recognition and modelled by a hierarchical set of carefully chosen swimmer descriptors. An effective alarm generation methodology is then developed to enable the timely detection of genuine water crises while minimizing the number of false alarms. The system has been tested on numerous instances of simulated water crises and potential false alarm scenarios with encouraging results.
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