A New Feature Clustering Method for Object Detection with an Active Camera (WA-P1)

Author(s) : Christian Micheloni (Department of Computer Science, University of Udine, Italy) Gian Luca Foresti (Department of Computer Science, University of Udine, Italy) Flavio Alberti (Department of Computer Science, University of Udine, Italy)

Abstract : Feature based methods for ego-motion estimation are widely used in computer vision but they must deal with errors in feature tracking. In this paper, we propose a robust real-time method for ego-motion estimation by assuming a affine motion of the background from the previous to the current frame. A clustering technique has been studied and applyed on image's subareas to select in a fast and reliable way three features for the affine transform computation. The previous frame after being wrapped according to the comupted affine transform is processed with the current frame by a change detection method in order to locate mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.

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