Summarization and Indexing of Home and Wearable Video

以电子方式记录生活日志，让我们可以详细记录我们的日常生活。到目前为止，我们只能依靠人类的记忆来记录和记住我们的日常经历。我们往往很快就失去了我们的经验的细节。我们用视频和照片记录旅行和家庭活动等特殊场合。除了这些特殊的事件，我们很少记录日常的经验，这是我们生活的主要部分;我们所做的最多的是写一个简短的日记。随着可穿戴、无处不在、移动的多媒体相关技术的不断发展，我们相信我们将能够自动捕捉和记录我们的日常经历。在本项目中，我们一直在研究通过各种传感器，如GPS（定位）、陀螺仪、加速度传感器（运动）、生理传感器（脑电波）、注释、文档、电子邮件和网页，通过音频和视频捕捉生活日志。检索是生命日志系统中最重要的问题。捕获的数据量非常大，问题是找到或浏览数据，以便我们可以查看我们体验的细节。记住我们经历的背景比记住它们的细节更容易，因此背景可以为索引信息提供有价值的关键。我们一直在开发一个基于上下文的检索系统，通过它，我们可以导航的参数，如时间，位置，一个人的行为，并通过注释，文档，电子邮件和网页的话大量的视频数据。我们可以通过城镇数据库中包含的商店和商店的名称搜索视频。

Capturing a life log by electronic means enables us to record our daily life in detail. To date we have only relied on our human memory to record and remember our daily experiences. We tend to quickly lose the details of our experiences. We record special occasions such as travels and family events with videos and photos. Excluding such special events, we rarely record the daily experiences that are the major part of our life ; what we do at most is writing a short diary. Because of developing technologies related to wearable, ubiquitous, mobile multimedia, we believe that we will be able to automatically capture and record our daily experiences.In this project, We have been investigating capturing a life log by audio and video with various sensors such as a GPS (location), gyros, acceleration sensors (motion), physiological sensors (brain wave), annotations, documents, emails, and web pages. Retrieval is the most important problem for the life log system. The amount of data captured is very large, and the problem is to find or navigate through the data so that we can watch the details of our experiences. Remembering the context of our experiences is easier than remembering details of them, so the context can provide valuable keys for indexing information. We have been developing a context-based retrieval system, by which we can navigate the huge amount of video data by parameters such as time, location, a person's behavior, and through words of annotations, documents, emails and web pages. We can search the video by the names of shops and stores that are contained in the town database.