Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors.

中文翻译：

---

InSocialNet：用于角色（事件视频）的交互式视觉分析

角色事件视频具有丰富的信息，但是在故事层面很难理解。角色的社会角色和行为模式在很大程度上取决于角色和背景事件之间的相互作用。了解它们需要长时间分析视频内容，这超出了当前用于分析短时动态的算法的能力。在本文中，我们提出了InSocialNet，这是一种用于分析角色事件视频内容的交互式视频分析工具。它通过使用面部表情和表情识别的角色事件视频自动动态地构建社交网络，并为视频内容的交互式分析提供可视界面。与后端的社交网络分析一起，InSocialNet支持用户调查角色，他们的关系，输入视频中的社交角色，派系和事件。我们进行案例研究，以证明InSocialNet在协助从角色事件视频中收获丰富信息方面的有效性。我们认为，当前的原型实现方式可以扩展到电影分析以外的应用程序，例如社会心理学实验，以帮助了解人群的社会行为。