Automatic Interactive Video Authoring Method via Object Recognition

摘要

Interactive video is a type of video which provides interactions for obtaining video related information or participating in video content. However, authors of interactive video need to spend much time to create the interactive video content. Many researchers have presented methods and features to solve the time-consuming problem. However, the methods are still too complicated to use and need to be automated. In this paper, we suggest an automatic interactive video authoring method via object recognition. Our proposed method uses deep learning based object recognition and an NLP-based keyword extraction method to annotate objects. To evaluate the method, we manually annotated the objects in the selected video clips, and we compared proposed method and manual method. The method achieved an accuracy rate of 43.16% for the whole process. This method allows authors to create interactive videos easily.