Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos
The availability of low-cost and high-quality wearable cameras combined with the unlimited storage capacity of video-sharing websites have evoked a growing interest in First-Person Videos. Such videos are usually composed of long-running unedited streams captured by a device attached to the user body, which makes them tedious and visually unpleasant to watch. Consequently, it raises the need to provide quick access to the information therein. We propose a Sparse Coding based methodology to fast-forward First-Person Videos adaptively. Experimental evaluations show that the shorter version video resulting from the proposed method is more stable and retain more semantic information than the state-of-the-art. Visual results and graphical explanation of the methodology can be visualized through the link: https://youtu.be/rTEZurH64ME
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