Semantic Interpretation of Multi-Modal Human-Behaviour Data
We present a commonsense theory of space and motion for representing and reasoning about motion patterns in video data, to perform declarative (deep) semantic interpretation of visuo-spatial sensor data, e.g., coming from object tracking, eye tracking data, movement trajectories. The theory has been implemented within constraint logic programming to support integration into large scale AI projects. The theory is domain independent and has been applied in a range of domains, in which the capability to semantically interpret motion in visuo-spatial data is central. In this paper, we demonstrate its capabilities in the context of cognitive film studies for analysing visual perception of spectators by integrating the visual structure of a scene and spectators gaze acquired from eye tracking experiments.