A probabilistic logic programming event calculus. Issue 2 (March 2015)

• Record Type: Journal Article
• Authors: SKARLATIDIS, ANASTASIOS; ARTIKIS, ALEXANDER; FILIPPOU, JASON; PALIOURAS, GEORGIOS
• Is Part Of: Theory and practice of logic programming. Volume 15:Issue 2(2015)
• Page Start: 213
• Page End: 245
• Publication Date: 2015-03
• Subjects: Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068413000690
• Languages: English
• ISSNs: 1471-0684

Abstract

<abstract abstract-type="normal"> <title>Abstract</title> <p>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 an 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.</p> </abstract>