Personal Multi-view Viewpoint Recommendation based on Trajectory Distribution of the Viewing Target
Xueting Wang, Kensho Hara, Yu Enokibori, Takatsugu Hirayama, Kenji Mase · 2016
Multi-camera videos with abundant information and high flexibility are expected to be useful in a wide range of applications, such as surveillance systems, web lecture broadcasting, concerts and sports viewing, etc. Viewers can enjoy a high-presence viewing experience of their own choosing by means of virtual camera switching and controlling viewing interfaces. However, some viewers may feel annoyed by continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint-recommending method designed especially for soccer games. This method focuses on a viewer's personal preference for viewpoint-selection, instead of common and professional editing rules. We assume that the different trajectory distributions cause a difference in the viewpoint selection according to personal preference. We therefore analyze the relationship between the viewer's personal viewpoint selecting tendency and the spatio-temporal game context. We compare methods based on a Gaussian mixture model, a general histogram+SVM and bag-of-words+SVM to seek the best representation for this relationship. The performance of the proposed methods are verified by assessing the degree of similarity between the recommended viewpoints and the viewers' edited records.