Camera operation estimation from video shot using 2D motion vector histogram

This paper presents a novel technique for classifying several camera operations in videos. First of all, we obtain a series of 2D motion vector (MV) fields by applying an existing MV estimation method. Then, a 2D MV histogram is generated in polar coordinates. The histogram shows that how many MVs in each frame share the similar magnitude and orientation. These two MV features are utilized simultaneously to classify the camera operations by representing on the 2D histogram. The proposed method can detect not only single camera operations but also a combination of two camera operations. The 2D histogram can describe the speed of the camera operations. Moreover, the 2D MV field itself can separate zoom-in and zoom-out camera operations that may produce exactly the same pattern in the 2D MV histogram. Especially, separating zoom-in and zoom-out camera operations because these two operations produce a similar 2D histogram. The proposed method can achieve a processing time of 5-10 millisecond per frame for a low-resolution video, while it takes 40-80 millisecond for a high-resolution video.