Abstract
We describe a framework for automated complex human behavior recognition, illustrating important concepts with specific examples drawn from our work on a unique platform designed to understand water crises related behaviors in a public swimming pool. We argue for a hierarchical representation, leveraging on quantitative descriptors to model a behavior's intermediate semantics. Complex behaviour inference is then demonstrated using a novel regression-based approach based on a modified version of a functional link network, which learns quickly and classifies accurately in comparison with other competing decision making schemes.
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