Traffic Light Detection for Driving Environment Recognition Using Omnidirectional Camera
Object Detection is an important part of autonomous driving system. A popular type of autonomous vehicle requires a minimum sensor configuration. Since omnidirectional camera has a large angle of view, it is suitable for autonomous vehicle. However, the omnidirectional image is distorted in the shape. Therefore, it is difficult to apply existing image processing and machine learning. In this research, the authors focus on the fact that the omnidirectional image is distorted in the shape but the color does not change and they use neural networks for object detection, considering that neural networks can learn distorted shapes.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
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Authors:
- Ishida, Yasuyuki
- Sofian, Mohd Hafiz Hilman bin Mohammad
- Ito, Toshio
- Publication Date: 2021-5
Language
- English
- Japanese
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: pp 695-700
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Serial:
- Transactions of Society of Automotive Engineers of Japan
- Volume: 52
- Issue Number: 3
- Publisher: Society of Automotive Engineers of Japan
- ISSN: 0287-8321
- EISSN: 1883-0811
- Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Color; Detection and identification system applications; Image analysis; Image processing; Neural networks; Shape; Traffic signals
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01775037
- Record Type: Publication
- Source Agency: Japan Science and Technology Agency (JST)
- Files: TRIS, JSTAGE
- Created Date: Jun 24 2021 4:40PM