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The recognition of objects that appear in a video sequence is an essential aspect of any video content analysis system. We present an approach which classifies a segmented video object based on its appearance (object views) in successive video frames. The classification is performed by matching curvature features of the contours of these object views to a database containing preprocessed views of prototypical objects using a modified curvature scale space technique. By integrating the results of a number of successive frames and by using the modified curvature scale space technique as an efficient representation of object contours, our approach enables the robust, tolerant and rapid object classification of video objects.