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Understanding videos at scale: How to extract insights for business research

机译：在规模上了解视频：如何提取商业研究的见解

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摘要

Video content has become a major component of total internet traffic. Growing bandwidth and computational power conspire with an increasing number of video editing tools, smartphones, and online platforms that have facilitated video production, distribution, and consumption by businesses and consumers alike. This makes video content relevant across business research disciplines. However, analyzing videos can be a cumbersome manual task. Automated techniques are scattered across technical publications and are often not directly accessible to business researchers. This article synthesizes the current state of the art and provides a consolidated tool to efficiently extract 109 video-based variables, requiring no programming knowledge. The variables include structural video characteristics such as colorfulness as well as advanced content-related features such as scene cuts or human face detection. The authors discuss the research potential of video mining, the types of video features of likely interest, and illustrate application using a practical example.
机译：视频内容已成为互联网流量总数的主要组成部分。随着视频编辑工具，智能手机和在线平台的越来越多的视频编辑工具，智能手机和在线平台，越来越多的带宽和计算能力伴随着企业和消费者的消费者。这使得视频内容与业务研究学科相关。但是，分析视频可以是一个麻烦的手动任务。自动化技术分散在技术出版物上，通常不能直接访问商业研究人员。本文综合了本领域的当前状态，并提供了一个综合工具，以有效提取109个基于视频的变量，不需要编程知识。变量包括诸如丰富多彩的结构视频特征以及高级内容相关的特征，例如场景剪切或人脸检测。作者讨论了视频挖掘的研究潜力，可能的兴趣的视频特征类型，并使用实际示例说明应用。

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