Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades, and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based analysis pipeline. Then, we suggest a taxonomy of the existing algorithms and provide a systematic review of the relevant literature that shows the evolution of the deep-learning-based video summarization technologies and leads to suggestions for future developments. We then report on protocols for the objective evaluation of video summarization algorithms, and we compare the performance of several deep-learning-based approaches. Based on the outcomes of these comparisons, as well as some documented considerations about the amount of annotated data and the suitability of evaluation protocols, we indicate potential future research directions.

中文翻译：

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使用深度神经网络进行视频摘要：调查


视频摘要技术旨在通过选择视频内容中信息最丰富的部分来创建简洁而完整的概要。在过去的几十年里，已经开发了几种方法，当前最先进的方法是依赖现代深度神经网络架构的方法。这项工作重点关注该领域的最新进展，并对现有的基于深度学习的通用视频摘要方法进行了全面的调查。在介绍了视频摘要技术发展背后的动机之后，我们制定了视频摘要任务并讨论了典型的基于深度学习的分析流程的主要特征。然后，我们建议对现有算法进行分类，并对相关文献进行系统回顾，展示基于深度学习的视频摘要技术的演变，并为未来的发展提出建议。然后，我们报告用于客观评估视频摘要算法的协议，并比较几种基于深度学习的方法的性能。根据这些比较的结果，以及一些关于注释数据量和评估协议适用性的记录考虑因素，我们指出了未来潜在的研究方向。