Fast-Forward Methods for Egocentric Videos: A Review - Silva, Michel; Ramos, Washington; Neves, Alan; Araujo, Edson; Campos, Mario; Nascimento, Erickson (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2019: Anais da XXXII Conference on Graphics, Patterns and Images, 2019)
The emergence of low-cost, high-quality personal wearable cameras combined with the almost unlimited storage capacity of video-sharing websites have evoked a growing interest in First-Person Videos. A first-person video is usually composed of long-running unedited streams captured by a device attached to the user body, which makes it tedious and visually unpleasant to watch. Consequently, there is a rise in the need to provide quick access to the information therein. Video summarization techniques create a summary of the video in the format of a sequence of keyframes or video skimmings with the most representative parts of the narrative. The main drawback of applying Video Summarization technique is the loss of temporal continuity, which breaks the context of the narrative represented in the video. New Hyperlapse methods have proposed different adaptive frame sampling to accelerate First-Person Videos concerning both the visual instability of the final output video and temporal continuity. Nevertheless, Hyperlapse techniques neglect the semantic load of the video, treating the whole video as equally relevant. Since the scope is First-Person Videos, some parts of them are undoubtedly more important than others, so they should have their proper attention. Semantic Hyperlapse techniques address this lack of care with the semantic load of the videos by creating emphasis on the relevant information in the output video. Such techniques aim to create visually pleasant shorter videos and also emphasize the semantic portions. In this tutorial, we will cover the context behind the Fast-Forward methods, starting with Video Summarization, passing through Fast-Forward and Hyperlapse, until reaching the Semantic Hyperlapse methods. During the introduction of the concepts, we will interact with the audience performing a hands-on practice using the acceleration methods. Finally, we will present some datasets proposed in the literature and discuss future directions of the area.