A fast algorithm for detection of camera motion

Most image analysis/understanding applications require accurate computation of camera motion parameters. However, in multimedia applications, particularly in video parsing, the exact camera motion parameters such as the panning and/or zooming rates are not needed. The detection-i.e., a binary decision-of camera motion is all that is required to avoid declaring a false scene change. As camera motions can induce false scene changes for video parsing algorithms, we propose a fast algorithm to detect such camera motions: camera zoom and pan. As the algorithm is only expected produce a binary decision, without the exact panning/zooming rates, the proposed algorithm runs on a reduced data set, namely the projection data. The algorithm begins with a central portion of the image and computes the projection data (or the line integrals along the x- or y- axis) to turn the 2-D image data into a 1-D data. This projected 1-D data is further processed via correlation processing to detect camera zoom and pan. Working with projection data saves processing time by an order of magnitude, since for instance, a 2-D correlation takes N² multiplies per point, however a 1-D correlation takes N multiplies per point. The efficacy of the proposed algorithm is tested for a number of image sequences and the algorithm is shown to be successful in detecting camera motions. The proposed algorithm is expected to be beneficial for video parsers working with Motion-JPEG data stream where motion vectors are not available.