Learning probabilistic relational models from sequential video data with applications in table-top and card games
Laura Antanas, Martijn van Otterlo, Luc De Raedt, Ingo Thon · Lirias · 2009
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.