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dc.contributor.authorWu, Tai-Enen_US
dc.contributor.authorTsai, Chia-Chien_US
dc.contributor.authorGuo, Jiun-Inen_US
dc.date.accessioned2018-08-21T05:57:03Z-
dc.date.available2018-08-21T05:57:03Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2309-9402en_US
dc.identifier.urihttp://hdl.handle.net/11536/146971-
dc.description.abstractNowadays, the machine learning for object detection is growing popular and widely adopted in many fields, such as surveillance, automotive, passenger flow analysis, etc. This paper focuses on the research of Lidar/camera sensor fusion technology for pedestrian detection to ensure extremely high detection accuracy. In order to reduce the false-positive rate and the object occlusion problem, which usually happened in camera-based pedestrian detection, we use 3D point cloud returning from Lidar depth sensor to do the further examination on the object's shape. The proposed Lidar/camera sensor fusion design complements the advantage and disadvantage of two sensors such that it is more stable in detection than others. The region proposal is given from both sensors, and candidate front two sensors are also going to the second classification for double checking. It maximums the detection rate and achieves average 99.16% detection accuracy for pedestrian detection.en_US
dc.language.isoen_USen_US
dc.titleLiDAR/Camera Sensor Fusion Technology for Pedestrian Detectionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017)en_US
dc.citation.spage1675en_US
dc.citation.epage1678en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000425879400305en_US
Appears in Collections:Conferences Paper