A Hierarchical Human Activity Recognition Framework Based on Automated Reasoning

Conventional human activity recognition approaches are mainly based on machine learning methods, which are not working well for composite activity recognition due to the complexity and uncertainty of real scenarios. We propose in this paper an automated reasoning based hierarchical framework for human activity recognition. This approach constructs a hierarchical structure for representing the composite activity by a composition of lower-level actions and gestures according to its semantic meaning. This hierarchical structure is then transformed into logical formulas and rules, based on which the resolution based automated reasoning is applied to recognize the composite activity given the recognized lower-level actions by machine learning methods.