Affiliation:
1. Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Athens, 15310, Greece
2. Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece
Abstract
We present a system for recognising human behaviour given a symbolic representation of surveillance videos. The input of our system is a set of time-stamped short-term behaviours, that is, behaviours taking place in a short period of time — walking, running, standing still, etc — detected on video frames. The output of our system is a set of recognised long-term behaviours — fighting, meeting, leaving an object, collapsing, walking, etc — which are pre-defined temporal combinations of short-term behaviours. The definition of a long-term behaviour, including the temporal constraints on the short-term behaviours that, if satisfied, lead to the recognition of the long-term behaviour, is expressed in the Event Calculus. We present experimental results concerning videos with several humans and objects, temporally overlapping and repetitive behaviours. Moreover, we present how machine learning techniques may be employed in order to automatically develop long-term behaviour definitions.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Artificial Intelligence
Cited by
12 articles.
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