DocumentCode
154482
Title
Bionic vision inspired on-road obstacle detection and tracking using radar and visual information
Author
Xiao Wang ; Linhai Xu ; Hongbin Sun ; Jingmin Xin ; Nanning Zheng
Author_Institution
Inst. of Artificial Intell. & Robot., Xi´an Jiaotong Univ., Xi´an, China
fYear
2014
fDate
8-11 Oct. 2014
Firstpage
39
Lastpage
44
Abstract
Inspired by the cooperation between the cone cell and the rod cell in human vision system, this paper presents a novel method for fusing millimeter wave (MMW) radar and monocular vision sensor information for on-road obstacle detection. The MMW radar performs similarly as the cone cell to indicate potential target region in the view, while the camera plays the same role as that of the rod cell to obtain precise target contour and to allow tracking over frames. Using real-world data collected from on-road vehicles, experimental evaluations have shown that, our proposed system could detect on-road obstacles with high detection rate and low false-alarm rate, yet the overall image processing time is significantly reduced compared to conventional approaches. In addition, the system has been applied on our unmanned vehicle for the past two years´ “Future Challenge” autonomous vehicles competition.
Keywords
computer vision; image fusion; image sensors; object detection; object tracking; remotely operated vehicles; traffic engineering computing; Future Challenge autonomous vehicles competition; MMW radar; bionic vision; cone cell; detection rate; false-alarm rate; human vision system; image processing time; information fusion; millimeter wave radar sensor; monocular vision sensor; on-road obstacle detection; on-road obstacle tracking; radar information; rod cell; unmanned vehicle; visual information; Cameras; Radar detection; Radar imaging; Radar tracking; Target tracking; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on
Conference_Location
Qingdao
Type
conf
DOI
10.1109/ITSC.2014.6957663
Filename
6957663
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