This paper presents a novel manifold learning approach for high dimensional data, with emphasis on the problem of motion tracking in video sequences. In this problem, the samples are time-ordered, providing additional information that most current methods do not take advantage of. Additionally, most methods assume that the manifold topology admits a single chart, which is overly restrictive. Instead, the algorithm can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are combined in a probabilistic fashion using Gaussian process regression. Thus, the algorithm is termed herein as Gaussian Process Multiple Local Models (GP-MLM). Additionally, the paper describes a multiple filter architecture where standard filtering techniques, e.g. particle and Kalman filtering, are combined with the output of GP-MLM in a principled way. The performance of this approach is illustrated with experimental results using real video sequences. A comparison with GP-LVM [29] is also provided. Our algorithm achieves competitive state-of-the-art results on a public database concerning the left ventricle (LV) ultrasound (US) and lips images.
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