2007 Fiscal Year Final Research Report Summary
Research on recognition mechanisms of low-quality images for multi-viewpoint video surveillance
| Project/Area Number |
16300054
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | Nagoya University |
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Principal Investigator |
MURASE Hiroshi Nagoya University, Graduate School of Information Science, Professor (90362293)
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| Co-Investigator(Kenkyū-buntansha) |
IDE Ichiro Nagoya University, Graduate School of Information Science, Associate Professor (10332157)
MEKADA Yoshito Chukyo University, Faculty of Life Science and Technology, Professor (00282377)
SUENAGA Yasuhito Nagoya University, Graduate School of Infornation Science, Professor (60293643)
MORI Kensaku Nagoya University, Graduate School of Information Science, Associate Professor (10293664)
HIRANO Yasushi Nagoya University, Information Technology Center, Associate Professor (90324459)
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| Project Period (FY) |
2004 – 2007
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| Keywords | Multiple viewpoint / Low-resolution / Video / Portable camera / Image recognition |
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| Research Abstract |
As the Information and Communication Technologies develop, numerous visual sensors such as camera-equipped mobile phones, car-mounted cameras, surveillance cameras now exist ubiquitously in the real-world environment. Accordingly, surveillance technologies that realize a safe and pleasant society by making use of data obtained from these sensors are needed. Images obtained from the real-world with small camera devices, however, have problems such as insufficient resolutions, and the variance of the appearance of objects due to lighting conditions and different viewpoints. This project aimed at developing a method that accurately recognizes from "Very-low quality image" that even human eye& cannot recognize from a single image, by integrating multimodal information obtained from different viewpoints and/or at different timings. The following are the actual fruits of the project (1) Proposed methods for low-quality character recognition, that combine similarities obtained from consecutive frames by robust statistical analysis, and that synthesizes degraded characters for training (2) Proposed a method for deciding optimal arrangements of multiple cameras according to the class of objects to be recognized. Proposed also a method that recognizes well with a small number of training samples. (3) Proposed methods for car-mounted cameras such as detection of streetscape changes based on streetscape video data obtained at different timings, recognition of traffic signs from degraded video data, and weather recognition based on raindrop detection on the front shield of a running car.
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Research Products
(8 results)
