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

摘要

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.