Tracking Visitor´s Fields of Interest in Large Scale Art Installations

Aurora is a large-scale kinetic art installation that reacts to human presence directly, with sensors triggering outputs, and indirectly, by modifying output behaviour rules. This paper describes a novel method for estimating visitors´ fields of interest, their attention to specific parts of the installation, with a future goal of using this measure as a fitness function for output behaviour modification based on genetic algorithms. Due to constraints in Aurora, distributed overhead distance sensors were used as the sensory inputs. A low resolution height graph of the space below the installation is created, and the active sensors are clustered into groups. The height graph and sensor groups are used to produce a probability map of possible visitor locations. Based on these, particle filters are created to estimate the visitors´ state, and by extension their fields of interest. Using this overall strategy for tracking and interest prediction, an average prediction accuracy of 92% is found when compared to a set of simulated people moving within a simulated space.