Precise navigation map is crucial in many fields. This paper proposes a panorama based method to detect and recognize lane\nmarkings and traffic signs on the road surface. Firstly, to deal with the limited field of view and the occlusion problem, this paper\ndesigns a vision-based sensing systemwhich consists of a surround viewsystemand a panoramic system. Secondly, in order to detect\nand identify traffic signs on the road surface, sliding window based detection method is proposed. Template matching method and\nSVM (Support Vector Machine) are used to recognize the traffic signs. Thirdly, to avoid the occlusion problem, this paper utilities\nvision based ego-motion estimation to detect and remove other vehicles.As surround viewimages contain less dynamic information\nand gray scales, improved ICP (Iterative Closest Point) algorithm is introduced to ensure that the ego-motion parameters are\nconsequently obtained. For panoramic images, optical flow algorithm is used.The results from the surround view system help to\nfilter the optical flow and optimize the ego-motion parameters; other vehicles are detected by the optical flow feature. Experimental\nresults show that it can handle different kinds of lane markings and traffic signs well.
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