Title: Probabilistic logical sequence learning for video
Authors: Antanas, Laura
Thon, Ingo
van Otterlo, Martijn
Landwehr, Niels
De Raedt, Luc #
Issue Date: Jul-2009
Host Document: Online proceedings of the International Conference on Inductive Logic Programming edition:19
Conference: Inductive Logic Programming edition:19 location:Leuven, Belgium date:2-4 July 2009
Article number: 37
Abstract: Understanding 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, while sensor data usually consists of many low-level features. We first propose a method to obtain a logical representation of real-world, dynamic scenes based on input video stream solely. We focus on representing the video data using probabilistic relational sequences as a natural way to incorporate sensor uncertainty. They allow us to work with structured terms, and in addition they capture the inherent uncertainty of object detection. Further on, we employ r-grams as the probabilistic logical learning model for this application. In a first step we use r-grams in a simple setting and we show their viability in card games. We also show how r-grams can be upgraded to deal with uncertain observations.
Description: accepted as poster
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
# (joint) last author

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