EAGER: Multimodal Corpus for Vision-Based Meeting Analysis

这个项目探讨了一个多模态语料库的视觉为基础的会议分析。研究小组正致力于：（1）从磁带中提取数据，并将其组织成多媒体数据库;（2）开发数据库可视化和分析工具，以支持模型开发;以及（3）开发基于代理的算法，以提取手和头部跟踪信息，以便可以在数据上建立更高级别的模型。该项目提供的数据集被组织成一个可用的语料库，具有许多独特的属性，如在心理语言/心理社会层面的社会角色状态，每次会议的目的，并在视频水平的运动跟踪数据的形式收集与视频同步，用于开发和测试新的算法。所开发的工具提高了对多视图组人类行为的多媒体数据库的访问。基于Agent的方法为视频标注提供了一种新的途径。 该项目开发的工具和算法可以应用于许多其他应用程序。例如，这些工具可以应用于分析课堂行为和学习场景。该项目为本科生和研究生提供研究机会，包括妇女和代表性不足人口的个人。该项目通过出版物、演示文稿、网络存在和更广泛的协作互动，向用户社区伸出援手。

This project explores a multimodal corpus for vision-based meeting analysis. The research team is working on: (1) extracting the data from tapes and organizing them into multimedia databases; (2) developing a database visualization and analysis tool to support model development; and (3) developing an agent-based algorithm to extract hand and head tracking information so that higher level models may be built onto the data. The project provides datasets that are organized into a usable corpus with many unique properties, such as the ground truth at the psycholinguistic/psycho-social level of the social roles status, purpose of each meeting, and at the video level in the form of motion tracking data collected co-temporally with the video, for developing and testing new algorithms. The developed tools improve the access to the multimedia database of multi-view group human behavior. The agent-based approach provides a novel way in video annotation. The developed tools and algorithms from this project can be applied to many other applications. For example, the tools may be applied to analyze classroom behavior and in learning scenarios. The project provides research opportunities for undergraduate and graduate students including women and individuals from underrepresented populations. The project outreaches to the user communities through publications, presentations, web presence, and broader collaborative interactions.