In this paper, we present a novel learning-based algorithm for temporal segmentation of a video into clips based on both camera and scene motion, in particular, based on combinations of static vs. dynamic camera and static vs. dynamic scene. Given a video, we first perform shot boundary detection to segment the video to shots. We enforce temporal continuity by constructing a Markov Random Field (MRF) over the frames of each video shot with edges between consecutive frames and cast the segmentation problem as a frame level discrete labeling problem. Using manually labeled data we learn classifiers exploiting cues from optical flow to provide evidence for the different labels, and infer the best labeling over the frames. We show the effectiveness of the approach using user videos and full-length movies. Using sixty full-length movies spanning 50 years, we show that the proposed algorithm of grouping frames purely based on motion cues can aid computational applications such as recovering depth from a video and also reveal interesting trends in movies, which finds itself interesting novel applications in video analysis (time-stamping archive movies) and film studies.
Kowdle and Chen REF also train classifiers based on the dynamism of both scene and camera, and use them together with shot-boundary detection in order to segment videos into clips of consistent motion.
Learning to segment a video to clips based on scene and camera motion
ECCV
Computer Science