2020/06/10 by Alan Carvalho Neves, Alan C. Neves, Neves, Alan Carvalho +9 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Artificial intelligence #Computer graphics (images) #Computer science #Computer vision #Gaze #Video Analysis and Summarization #cs.CV
paper · pdf · doi:10.48550/arxiv.2006.05569
published in arXiv (Cornell University) (Cornell University) · Accepted for presentation at EPIC@CVPR2020 workshop
arxiv created 2020/06/10 · openalex publication_date 2020/06/10 · arxiv updated 2020/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The growing data sharing and life-logging cultures are driving an unprecedented increase in the amount of unedited First-Person Videos. In this paper, we address the problem of accessing relevant information in First-Person Videos by creating an accelerated version of the input video and emphasizing the important moments to the recorder. Our method is based on an attention model driven by gaze and visual scene analysis that provides a semantic score of each frame of the input video. We performed several experimental evaluations on publicly available First-Person Videos datasets. The results show that our methodology can fast-forward videos emphasizing moments when the recorder visually interact with scene components while not including monotonous clips.