Local Gaussian Processes for Pose Recognition from Noisy Inputs

Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they are limited due to their inability to model multi-modality caused by ambiguities and varying noise in the data set. For this reason techniques employing mixtures of local Gaussian processes have been proposed to allow multi-modal functions to be predicted accurately [11]. These techniques rely on the calculation of nearest neighbours in the input space to make accurate predictions. However, this becomes a limiting factor when image features are noisy due to changing backgrounds. In this paper we propose a novel method that overcomes this limitation by learning a logistic regression model over the input space to select between the local Gaussian processes. Our proposed method is more robust to a noisy input space than a nearest neighbour approach and provides a better prior over each Gaussian process predictio...

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Martin Fergie, Aphrodite Galata
• Keywords: BMVC 2010, Computer Vision, Gaussian, Input Space, Nearest Neighbour