Abstract

Multiple elements such as lighting, colors, dialogue, and cam- era motion contribute to the style of a movie. Among them, camera motion is commonly overlooked yet a crucial point. For instance, docu- mentaries tend to use long smooth pans whereas action movies usually have short and dynamic movements. This information, also referred to as global motion, could be leveraged by various applications in video clustering, stabilization, and editing. We perform analyses to study the in-class characteristics of these motions as well as their relationship with motions of other movie types. In particular, we model global motion as a multi-scale distribution of transformation matrices from frame to frame. Secondly, we quantify the difference between pairs of videos us- ing the KL-divergence of these distributions. Finally, we demonstrate an application modeling and clustering commercial and amateur videos. Experiments performed show advantage compared to the usage of some local motion-based approaches.