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With the development of computer processing capacity and the widespread use of high-quality cameras, the use of multiview videos is becoming prevalent 1). In particular, in large spaces, such as soccer stadiums, more information can be obtained by observing multiview videos than a single-view video. Thus, research on multi-view videos that observe the subjects from various viewpoints is actively being conducted 2)-4). Bullet-time video is a method used to browse multiview videos. This method presents the captured image almost as it is. Because it is possible to observe the target object while maintaining the quality/resolution of the captured image, Bullet-time videos are suitable for detailed observation such as scene analysis. When the situation of target objects changes, it is necessary to dynamically set the following to obtain a Bullet-time video of a dynamic scene. (1) focus on the captured space (attention point). (2) the direction to be observed (observation viewpoint). (3) establish the size to be observed (zoom value). In the following, the attention point, observation viewpoint, and zoom value are referred to as the camerawork parameters. In the previous Bullet-time video generation method 5), the camerawork parameters are manually set using some input device such as a mouse or keyboard. The observer must stop the video at the moment of multifaceted observation to set the camerawork parameters. Moreover, to dynamically and appropriately setting the camerawork parameters requires the knowledge of algorithms for generating Bullet-time videos and video production. Therefore, it is too complex and difficult for the general audience. We aim to solve the problems of generating Bullet-time videos automatically. This paper proposes an automatic multiview video switching method for dynamic scenes for large-scale spaces. As shown in the upper part of Fig. 1, under our shooting conditions, the cameras are arranged more Abstract Research on tactical and performance analysis utilizes videos of dynamic sports scenes. An effective multiview video switching method can support the analysis. Bullet-time video is a multiview video browsing approach. Because the image is presented almost as it is, it is suitable for high-quality observations of the subject from multiple directions. This paper proposes a multiview image switching method for understanding dynamic scenes in large-scale spaces such as soccer games. We develop a prediction model for the camerawork for shooting Bullet-time videos. The model using deep neural network, which can estimate a suitable viewpoint to observe the target scene from the position information of the soccer players, ball, and goals.

[Papers] Method of Multiview Video Switching for Soccer Game Analysis in Large Scale Space