Abstract

We present a novel method to generate salient montages from unconstrained videos, by finding “montageable moments” and identify- ing the salient people and actions to depict in each montage. Our method addresses the need for generating concise visualizations from the increas- ingly large number of videos being captured from portable devices. Our main contributions are (1) the process of finding salient people and mo- ments to form a montage, and (2) the application of this method to videos taken “in the wild” where the camera moves freely. As such, we demonstrate results on head-mounted cameras, where the camera moves constantly, as well as on videos downloaded from YouTube. Our approach can operate on videos of any length; some will contain many montage- able moments, while others may have none. We demonstrate that a novel “montageability” score can be used to retrieve results with relatively high precision which allows us to present high quality montages to users.