Qualitative and Quantitative Spatio-temporal Relations in Daily Living Activity Recognition

For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects and objects in an abstract and efficient manner. Unlike current state of the art approaches, our approach uses significantly fewer assumptions and does not require any knowledge about object types, their affordances, or the sub-level activities that high-level activities consist of. We perform an automatic feature selection process which provides the most representative descriptions of the learnt activities. We validated our method using these descriptions on the CAD-120 benchmark dataset consisting of video sequences showing humans performing daily real-world activities. The experimental results show the strength of our work which significantly outperforms the current state of the art benchmark.