A Probabilistic Logic Programming Event Calculus
Abstract
We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.
Cite
@article{arxiv.1204.1851,
title = {A Probabilistic Logic Programming Event Calculus},
author = {Anastasios Skarlatidis and Alexander Artikis and Jason Filippou and Georgios Paliouras},
journal= {arXiv preprint arXiv:1204.1851},
year = {2020}
}
Comments
Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journal