Adaptive Eigen-Backgrounds for Object Detection (TP-L3)

Author(s) : Jonathan Rymel (Digital Imaging Research Centre, UK) John-Paul Renno (Digital Imaging Research Centre, UK) Darrel Greenhill (Digital Imaging Research Centre, UK) James Orwell (Kingston University, UK) Graeme A. Jones (Kingston University, UK)

Abstract : ost tracking algorithms detect moving objects by comparing incoming images against a reference frame. Crucially, this reference image must adapt continuously to the current lighting conditions if objects are to be accurately differentiated. In this work, a novel appearance model method is presented based on the eigen-background approach. The image can be efficiently represented by a set of appearance models with few significant dimensions. Rather than accumulating the necessarily enormous training set to generate the eigen model, the described technique builds and adapts the eigen-model online evolving both the parameters and number of significant dimension. For each incoming image, a reference frame may be efficiently hypothesized from a subsample of the incoming pixels. A comparative evaluation that measures segmentation accuracy using large amounts of manually derived ground truth is presented.

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