A probabilistic interval-based event calculus for activity recognition

Artikis, Alexander, Makris, Evangelos, Paliouras, Georgios - Annals of Mathematics and Artificial Intelligence - Aug 2019.


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.

Go to publication:

This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 825070.

Let's get in touch!