Artificial Intelligence systems as a solution to subjective video sensing in Contemporary Performing Arts.

This paper discusses approaches to realtime motion tracking in contemporary dance. It outlines some problems with current techniques and proposes, through previous research, some alternative approaches that could provide much richer data sets for realtime sonification and visualization of choreographic patterns. 1. Background. While undertaking my PhD (Paine, 2002b), I developed a number of approaches to the mapping of gesture (tracked using video based systems, VNS1 and Cyclops), which identify issues involved in the sonification of gesture (Mulder et al., 1997) in a manner that creates a visceral engagement with the quality of outcome. The following are excerpts from that research as a way of contextualizing the need for the AI research discussed below. During 2000, while I was the Australia Council for the Arts, New Media Arts fellow at RMIT University, I began some research that attempted to expand the interactive environment research (Moser & MacLeod, 1996) I had been doing over the years prior to include dynamic levels of intelligence in video tracking of movement and behaviour patterns. Through my previous research involving interactive immersive sound installations and composing using interactive music systems for the Australian Dance company, Company in Space, I formed the opinion that for an interactive system to be substantially more complex and sophisticated than the current first or second order responses, a level of artificial intelligence had to be introduced between the sensing stage and the mapping of the sensed data to synthesi