Abstract
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 efficient manner. Unlike the state of the art, our approach uses significantly fewer assumptions and does not require 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 the method using these descriptions on the CAD-120 benchmark dataset, consisting of video sequences showing humans performing daily real-world activities. The method is shown to outperform state of the art benchmarks.
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Note that this is similar to the INDU calculus [34] which extends the interval calculus by discretising whether intervals in a before, meets or overlaps relationship are shorter, equal or longer than each other.
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The financial support of RACE (FP7-ICT-287752) and STRANDS (FP7-ICT-600623) projects is gratefully acknowledged.
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Tayyub, J., Tavanai, A., Gatsoulis, Y., Cohn, A.G., Hogg, D.C. (2015). Qualitative and Quantitative Spatio-temporal Relations in Daily Living Activity Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_8
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