Efficient Spatiotemporal Filtering and Modelling

Sammanfattning: The thesis describes novel methods for efficient spatiotemporal filtering and modeling. A multiresolution algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. The problem of how to properly process estimates with varying degree of reliability is addressed. An efficient spatiotemporal implementation of a certainty-based signal modeling method called normalized convolution is described. As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both target motion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modeled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested.

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