A probabilistic interval-based event calculus for activity recognition

Abstract

Activity recognition refers to the detection of temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. Various types of uncertainty exist in activity recognition systems and this often leads to erroneous detection. Typically, the frameworks aiming to handle uncertainty compute the probability of the occurrence of activities at each time-point. We extend this approach by defining the probability of a maximal interval and the credibility rate for such intervals. We then propose a linear-time algorithm for computing all probabilistic temporal intervals of a given dataset. We evaluate the proposed approach using a benchmark activity recognition dataset, and outline the conditions in which our approach outperforms time-point-based recognition.

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Acknowledgements

This work was supported by the INFORE project, which has received funding from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 825070. We would also like to thank Elias Alevizos for his feedback at the early stages of this work, and Evangelos Michelioudakis for his support in the experiments.

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Artikis, A., Makris, E. & Paliouras, G. A probabilistic interval-based event calculus for activity recognition. Ann Math Artif Intell (2019). https://doi.org/10.1007/s10472-019-09664-4

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Keywords

  • Action languages
  • Complex event recognition
  • Probabilistic logic programming

Mathematics Subject Classification (2010)

  • 68T37