A Probabilistic Logic Programming Event Calculus
This work addresses activity recognition in surveillance videos, but it appears incremental as it adapts existing methods to a probabilistic framework.
The paper tackles human activity recognition from video by developing a system that uses a probabilistic logic programming adaptation of the Event Calculus to handle uncertainty, achieving evaluation on a benchmark dataset.
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.