基於使用者偏好之影片快轉模型

Abstract

本論文提出一個新的影片瀏覽互動模型，稱之為自適快轉模型(adaptive fast-forwarding model)。此模型藉由分析影片中的語意化事件幫助使用者快速瀏覽影片；其設計是源自於類比車輛駕駛的行為模式：駕駛員若對窗外場景感興趣時，會降低車輛的速度，於不感興趣的場景則會加快車輛的速度。根據初步使用者實驗所觀察到的結果，我們設計出的影片播放系統具備以下的特徵： (1) 根據影片內容的複雜程度及語意化事件，此播放系統得以自動地調整影片播放速度。 (2) 此播放系統會學習使用者對於語意化事件的偏好以及該使用者對於影片播放速度的習慣。 (3) 此播放系統在快轉影片時不會跳過任何影片片段，避免使用者遺漏任何感興趣的內容。 此外，為了改善傳統速度控制模型在此影片瀏覽模型中不適用的情況，我們提出了絕對速度控制模型(absolute speed control model)，以增進影片播放速度的控制性。使用者測試的結果顯示，我們提出的系統－SmartPlayer比起傳統影片播放軟體的互動模式，在瀏覽部分特定種類之影片時可以帶來較好的使用經驗。

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.