| Project/Area Number |
23700192
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SONG Xuan 東京大学, 空間情報科学研究センター, 特任助教 (20600737)
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| Project Period (FY) |
2011 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2012: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Intelligent Surveillance / Abnormality Detection / Sensor Fusion / Online Learning |
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| Research Abstract |
Abnormal activity detection plays a crucial role in surveillance applications, and such system has become an urgent need for public security. However, the existing approaches are usually based on off-line and supervised method. In addition, most of them can only cover a small area and are difficult to apply into the large and high density public area. Therefore, in this project, we develop an online and unsupervised abnormal detection system, which can cover a large public area (more than 60×30m), perform the robust abnormal detection (over 85% accuracy) in high density and complex situations (hundred of persons at the same time).
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