Context-Dependent Viewpoint Sequence Recommendation System for Multi-view Video

Abstract

Multi-view videos shot using multiple cameras are highly interested due to their considerable flexibility in enhancing the quality of our daily viewing experience, especially for large-scale events. However, the increase in the number of cameras burdens even experts on suitable viewpoint selection. Therefore, we propose in this paper an automatic viewpoint sequence recommendation system to support multi-view viewpoint selecting with a soccer game example. Unlike existing methods, our proposed system focuses on context-dependency using viewpoint evaluation and transition processes by two types of agents: a camera agent and a producer agent. The camera agent evaluates the view quality based on scene context such as positions of ball and players in given production context such as camera position and user's preference. The producer agent selects the optimal set of viewpoints by taking account of the view quality and the production objectives. The context-dependent optimization has been performed to generate variable viewing patterns which are adequate to various scene and production contexts. Sequences generated by the system and the human selection were experimentally compared to confirm the effectiveness of our proposed system. Our recommendation system has the potential to satisfy both common and personal viewing preferences for sports games.