Automatic dominant camera motion annotation for video retrieval

An efficient method is derived to classify the dominant camera motions in video shots. Various 3-D camera motions including camera pan, tilt, zoom, Z-rotation, and translations are detected. The method is to analyze the optical flow in a decomposed manner. Images are divided into some sub-regions according to our camera model. The projected x and y components of optical flow are analyzed separately in the different sub-regions of the images. Different camera motions are recognized by comparing the computed result with the prior known patterns. The optical flow is computed by using the Lucas-Kanade method, which is quite efficient due to non-iteration computation. Our method is efficient and effective because only some mean values and standard deviations are used. The analysis and detailed description of our method is given in this paper. Experimental results are presented to show the effectiveness of our method