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A probabilistic logic programming event calculus

Content Provider	Scilit
Author	Skarlatidis, Anastasios Artikis, Alexander Filippou, Jason Paliouras, Georgios
Copyright Year	2014
Description	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.
Related Links	http://arxiv.org/abs/1204.1851 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E7486F289AD54D2FE36BA1F7ED8242D5/S1471068413000690a.pdf/div-class-title-a-probabilistic-logic-programming-event-calculus-div.pdf
Ending Page	245
Page Count	33
Starting Page	213
ISSN	14710684
e-ISSN	14753081
DOI	10.1017/s1471068413000690
Journal	Theory and Practice of Logic Programming
Issue Number	2
Volume Number	15
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2015-03-01
Access Restriction	Open
Subject Keyword	Theory and Practice of Logic Programming Artificial Intelligence Event Recognition Event Pattern Matching Event Calculus
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science Computational Theory and Mathematics Hardware and Architecture Software

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