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Title: Learning probabilistic relational models from sequential video data with applications in table-top and card games
Authors: Antanas, Laura
van Otterlo, Martijn
De Raedt, Luc
Thon, Ingo #
Issue Date: 18-May-2009
Host Document: Proceedings of the Annual Belgian-Dutch Conference on Machine Learning edition:18 pages:105-106
Conference: Belgian-Dutch Conference on Machine Learning edition:18 location:Tilburg date:18-19 May 2009
Article number: 32
Abstract: Being able to understand complex dynamic scenes of real-world activities from low-level sensor data is of central importance for intelligent systems. The main difficulty lies in the fact that complex scenes are best described in high-level, logical formalisms, whereas sensor data usually consists of many low-level feature values. In this work, we consider the problem of learning high-level, logical descriptions of dynamic scenes based on input video stream solely. In order to learn such general patterns, two important problems must be tackled and their solutions combined: obtaining high-level, logical representations from video data and learning probabilistic logical models of dynamic scenes. This setting opens new research directions. We focus on representing the video data using probabilistic relational sequences as a natural way to incorporate sensor information in real-world tasks. They allow to work with structured terms, but in addition they capture the inherent uncertainty of object detection. Further on, we employ relational sequence learning methods for this type of video representation. We propose table-top and card games as application domain.
Description: extended abstract
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
# (joint) last author

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