基於使用者偏好之影片快轉模型 User-preference-based Video Fast-forwarding Model

In this thesis we propose a new video interaction model called adaptive fast-forwarding to help people quickly browse videos with predefined semantic rules. This model is designed around the metaphor of scenic car driving, in which the driver slows down near areas of interest and speeds through unexciting areas. Results from a preliminary user study of our video player suggest the following: • The player should adaptively adjust the current playback speed based on the complexity of the present scene and predefined semantic events. • The player should learn user preferences about predefined event types as well as a suitable playback speed. • The player should fast-forward the video continuously with a playback rate acceptable to the user to avoid missing any undefined events or areas of interest. Furthermore, we provide the absolute speed control model with the analog controller to enhance the experience of speed control. Our user study results suggest that for certain types of video, our system SmartPlayer yields better user experiences in browsing and fast-forwarding videos than existing video players’ interaction models.