Fusion of Perceptual Cues using Covariance Estimation
Jamie Sherrah, Shaogang Gong · 1999
The paradigm of perceptual integration provides robust solutions to computer vision problems. By combining the outputs of multiple vision modules, the assumptions and constraints of each module are factored out to result in a more robust system overall. The integration of dierent modules can be regarded as a form of data fusion. To this end, we propose a framework for fusing dierent information sources through estimation of covariance from observations. The framework is demonstrated in a pose tracking method that fuses similarity-to-prototypes measures and skin colour to track head pose and face position. The use of data fusion through covariance introduces constraints that allow the tracker to robustly estimate head pose and track face position. Contact Author Jamie R. Sherrah Contact Address Department of Computer Science Queen Mary and Westeld College Mile End, E1 4NS London UK Contact email [email protected] Contact Tel. (+44) (0)171 975 5230 Contact Fax (+44) (0)181 980 Bri...