Robust camera motion estimation and classification for video analysis

Camera motion estimation is very important for indexing and retrieving video information. In this paper, we propose a robust camera motion estimation and classification algorithm. Our camera motion estimation algorithm consists of optical flow computation, iterative RANSAC (RANdom Sample Consensus) multiple motion estimation, and lone-term camera motion estimation through a shortest-path search. In this approach, we first estimate multiple global affine motions from the computed optical flow field for every frame in the video sequence. Then, the long-term camera motion is determined from searching, a shortest path in a graph of cascaded nodes of global motions. After the camera motion is determined for the whole video, we apply an artificial neural network to classify the camera motion type. This neural network is trained from a large set of different types of camera motion data. We show accurate camera motion classification results through experiments on real videos.