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A robust approach to global motion estimation for content-based video analysis

With the multimedia content description interface MPEG-7, we have powerful tools for video indexing, based on which content-based search-and-retrieval with respect to separate shots and scenes in video can be performed. We especially focus on the parametric motion descriptor. The motion parameters, being finally coded in the descriptor values, require robust content extraction methods. In this paper, we introduce our approach to the extraction of global motion from video. For this purpose, we apply a constraint feature point selection and matching approach in order to find correspondences in images. Subsequently, an M-estimator is used for robust estimation of the motion model parameters. We evaluate the performance of our approach using affine and biquadratic motion models, also in comparison with a standard least-median-of-squares based approach to global motion estimation.