LiDAR/camera sensor fusion technology for pedestrian detection

Tai En Wu, Chia Chi Tsai, Jiun-In  Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1675-1678
Number of pages4
ISBN (Electronic)9781538615423
DOIs
StatePublished - 5 Feb 2018
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 12 Dec 201715 Dec 2017

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Conference

Conference9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
CountryMalaysia
CityKuala Lumpur
Period12/12/1715/12/17

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