TY - GEN
T1 - LiDAR/camera sensor fusion technology for pedestrian detection
AU - Wu, Tai En
AU - Tsai, Chia Chi
AU - Guo, Jiun-In
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Nowadays, 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 from 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.
AB - Nowadays, 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 from 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.
UR - http://www.scopus.com/inward/record.url?scp=85050464276&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2017.8282301
DO - 10.1109/APSIPA.2017.8282301
M3 - Conference contribution
AN - SCOPUS:85050464276
T3 - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
SP - 1675
EP - 1678
BT - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -