Viewing Support System for Multi-view Videos

Multi-view videos taken by multiple cameras from different angles are expected to be useful in a wide range of applications, such as web lecture broadcasting, concerts and sports viewing, etc. These videos can enhancing viewing experience of users' personal preference through means of virtual camera switching and controlling viewing interfaces. However, the increasing number of cameras burdens even experts on suitable viewpoint selection. Thus, my doctoral research goal is to construct a system providing convenient and high quality viewing support for personal multi-view video viewing. We intend to include 3 parts: automatic viewpoint sequence recommendation, multimodal user feedback analysis, and on-line recommendation updating. Prior works focused on automatic viewpoint sequence recommending considering contextual information and user preference. We proposed a context-dependent recommending model and improved by considering the spatio-temporal contextual information. Further work will concentrate on analyzing multimodal user feedback while viewing recommendations to detect the unsatisfactory timing and model the user preference of viewpoint switching. The switching records and multimodal feedback can be used for on-line recommendation updating to improve the personal viewing support.