2004 Fiscal Year Final Research Report Summary
Summarization and Indexing of Home and Wearable Video
| Project/Area Number |
15360201
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | The University of Tokyo |
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Principal Investigator |
AIZAWA Kiyoharu The University of Tokyo, Graduate School of Frontier Science, Department of Frontier Informatics, Professor, 大学院・新領域創成科学研究科, 教授 (20192453)
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| Project Period (FY) |
2003 – 2004
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| Keywords | lifelog / wearable / home video / indexing / context / content |
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| Research Abstract |
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.
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Research Products
(13 results)
