A New Method for Camera Motion Estimation in Video

Camera motion estimation is important for video semantic analysis and video classification. Camera motion can be classified into several classes: camera rotation, camera translation, and camera zoom, but ordinarily we consider only camera translation and camera zoom. Traditional approaches for camera motion parameters estimation is features-based, and they are dependent on the image quality and image features´ characteristic. Traditional approaches are not valid in the low-density texture images and deformed images. In this paper we consider image´s algebraic character, and introduce a robust approach for video camera motion parameters estimation. Scale parameter is extracted based on edge images´ singular value decomposition, and the translation parameters are extracted based on their phase difference of cross-power spectral in frequency domain. Experiments show that this method is simple and efficient, and it is independent of image features and robust in most cases.