Abstract
Video summarization is an ideal tool for skimming videos. Previous computational models extract explicit information from the input video, such as visual appearance, motion or audio information, in order to generate informative summaries. Eye gaze information, which is an implicit clue, has proved useful for indicating important content and the viewer’s interest. In this paper, we propose a novel gaze-aware deep learning model for video summarization. In our model, the position and velocity of the observers’ raw eye movements are processed by the deep neural network to indicate the users’ preferences. Experiments on two widely used video summarization datasets show that our model is more proficient than state-of-the-art methods in summarizing video for characterizing general preferences as well as for personal preferences. The results provide an innovative and improved algorithm for using gaze information in video summarization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
