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 suitability of evaluation protocols, we indicate potential future research directions.

中文翻译：

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使用深度神经网络的视频汇总：一项调查

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摘要。在过去的几十年中，已经开发出了几种方法，并且依靠依赖于现代深度神经网络体系结构的方法来代表当前的技术水平。这项工作着眼于该领域的最新进展，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调查。在介绍了视频汇总技术发展的动力之后，我们制定了视频汇总任务，并讨论了典型的基于深度学习的分析管道的主要特征。然后，我们建议对现有算法进行分类，并提供对相关文献的系统评价，这些文献显示了基于深度学习的视频摘要技术的发展，并为未来的发展提出了建议。然后，我们报告了用于视频摘要算法的客观评估的协议，并比较了几种基于深度学习的方法的性能。根据这些比较的结果，以及有关评估协议适用性的一些书面考虑，我们指出了潜在的未来研究方向。然后，我们报告了用于视频摘要算法的客观评估的协议，并比较了几种基于深度学习的方法的性能。根据这些比较的结果，以及有关评估协议适用性的一些书面考虑，我们指出了潜在的未来研究方向。然后，我们报告了用于视频摘要算法的客观评估的协议，并比较了几种基于深度学习的方法的性能。根据这些比较的结果，以及有关评估协议适用性的一些书面考虑，我们指出了潜在的未来研究方向。