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A probabilistic logic programming event calculus

Published online by Cambridge University Press:  22 May 2014

ANASTASIOS SKARLATIDIS ,
ALEXANDER ARTIKIS ,
JASON FILIPPOU  and
GEORGIOS PALIOURAS
ANASTASIOS SKARLATIDIS
Affiliation:
Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece (e-mail: [email protected], [email protected], [email protected], [email protected]) Department of Digital Systems, University of Piraeus, Piraeus, Greece
ALEXANDER ARTIKIS
Affiliation:
Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece (e-mail: [email protected], [email protected], [email protected], [email protected])
JASON FILIPPOU
Affiliation:
Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece (e-mail: [email protected], [email protected], [email protected], [email protected]) University of Maryland, College Park, MD, USA
GEORGIOS PALIOURAS
Affiliation:
Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece (e-mail: [email protected], [email protected], [email protected], [email protected])
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Abstract

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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.

Keywords

event recognitionevent pattern matchingEvent CalculusProbLog
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 15 , Issue 2: Probability, Logic and Learning , March 2015 , pp. 213 - 245
Copyright
Copyright © Cambridge University Press 2014 

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