| Project/Area Number |
08650308
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | The Unuversity of Tokyo |
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Principal Investigator |
MORI Taketoshi RCAST,Reseach Associate, 先端科学技術研究センター, 助手 (20272586)
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| Co-Investigator(Kenkyū-buntansha) |
MIYAZAKI Hideki RCAST,Reseach Associate, 先端科学技術研究センター, 助手 (10262114)
MIZOGUCHI Hiroshi Saitama Univ., Ass.Professor, 工学部, 助教授 (00262113)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1997: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1996: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Visual Tracking / Action Recognition / Behavior Understanding / Medical Care / Pointing Behavior / Medical Treatment / 行動理解 / 動作認識 / 視覚追跡 / 注視処理 / 医療行為 |
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| Research Abstract |
The purpose of this research is to realize a action recognition model which is a basic technology for human behavior understanding. Human behavior understanding is necessary for human symbiosis systems or human symbiotic robots. This research utilizes visual processing system which has high-speed visual tracking capability.
This research proposed three-revel hierarchy model. The top level is discrimination of behavior. The second is attention of action and the lowest is tracking of motion. The model is verified by expeiments which uses, human parts finding/tracking process, multiple attention region processing, and behavior discrimination process based on human behavior model-base.
The model has utilized for two types application. One is for Medical treatment understanding in medical care room. Most of medical ear/nose treatment can be understood using the proposed model with constructed system. The other application is pointing behavior (pointing gesture) understanding of sick person in a bed. The proposed model is used to understand one's gesture. In this application, color feature tracking revealed to be robust in complicated environment.
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