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.
|Title of host publication||2013 IEEE International Conference on Systems, Man, and Cybernetics: Proceedings|
|Place of Publication||United States|
|Publisher|| IEEE |
|Number of pages||5|
|Publication status||Published - 27 Jan 2014|
|Name||IEEE International Conference on Systems, Man, and Cybernetics: Proceedings|
2013 IEEE International Conference on Systems, Man, and Cybernetics (IEEESMC2013) ; Conference date: 13-10-2013