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The growing data sharing and life-logging cultures are driving anunprecedented increase in the amount of unedited First-Person Videos. In thispaper, we address the problem of accessing relevant information in First-PersonVideos by creating an accelerated version of the input video and emphasizingthe important moments to the recorder. Our method is based on an attentionmodel driven by gaze and visual scene analysis that provides a semantic scoreof each frame of the input video. We performed several experimental evaluationson publicly available First-Person Videos datasets. The results show that ourmethodology can fast-forward videos emphasizing moments when the recordervisually interact with scene components while not including monotonous clips.

A gaze driven fast-forward method for first-person videos