Visual Person Localization with Dynamic Neural Fields: Towards a Gesture Recognition System
For any visually-based interaction between persons and acting systems within a real-world environment the localization of a user by the system is a necessary condition. The presented work deals with this visual localization problem of a user concretely referred to the autonomous mobile robot system MILVA of our department. Since this system is applied under real-world conditions especially for the localization some proper techniques are needed which have an adequate robustness. In our opinion, this requires the combination of several components of saliency towards a multi-cue approach, consisting of structure- and color-based features .
This paper introduces one of them: the localization based on a typical shape of contour. A simple contour shape prototype model consists of an arrangement of oriented filters doing a piecewise approximation of the upper shape (head, shoulder) of a frontally aligned person. Applying such filter arrangement in a multiresolution manner, this leads to a robust localization of frontally aligned persons even in depth. The central problem of selecting the most promising (salient) image region is treated by means of a three-dimensional dynamic neural field performing a dynamic winner-take-all process (WTA, [1,6]).
After a successful localization of a person one can start a more detailed analysis of the gesture’s meaning: besides the recognition of static gestures we also concentrate on the acquisition and later the recognition of dynamic gestures.
KeywordsGabor Filter Localization Task Filter Arrangement Neural Field Static Gesture
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