Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos

  • Michel M. Silva UFMG
  • Mario F. M. Campos UFMG
  • Erickson R. Nascimento UFMG

Resumo


The availability of low-cost, high-quality personal wearable cameras combined with the unlimited storage capacity of video-sharing websites has evoked a growing interest in First-Person Videos (FPVs). 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, there is a rise in the need to provide quick access to the information therein. To address this need, efforts have been applied to the development of techniques such as Hyperlapse and Semantic Hyperlapse, which aims to create visually pleasant shorter videos and emphasize semantic portions of the video, respectively. The state-of-the-art Semantic Hyperlapse method SSFF, negligees the level of importance of the relevant information, by only evaluating if it is significant or not. Other limitations of SSFF are the number of input parameters, the scalability in the number of visual features to describe the frames, and the abrupt change in the speed-up rate of consecutive video segments. In this dissertation, we propose a parameter-free Sparse Coding based methodology to adaptively fast-forward First-Person Videos, that emphasize the semantic portions applying a multi-importance approach. Experimental evaluations show that the proposed method creates shorter version video retaining more semantic information, with fewer abrupt transitions of speed-up rates, and more stable final videos than the output of SSFF. Visual results and graphical explanation of the methodology can be visualized through the link: https://youtu.be/8uStih8P5-Y.

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Publicado
28/10/2019
SILVA, Michel M.; CAMPOS, Mario F. M.; NASCIMENTO, Erickson R.. Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 56-62. DOI: https://doi.org/10.5753/sibgrapi.est.2019.8302.