Obstacle Detection and Hierarchical Coherence Measurement of Roadway Disparity Map
Date Issued
2007
Date
2007
Author(s)
Shih, Yu-Chun
DOI
en-US
Abstract
This thesis aims at developing techniques of computational stereovision (CSV) for navigation and collision avoidance of autonomous vehicles or mobile robots. The main problem is to implement a CSV system to extract three-dimension information about roadway from stereo images. It is assumed the stereo camera looks obliquely down so that roadway is under its view. Stereo image pairs are captured and transformed sequentially into disparity maps in which roadway and obstacles are detected and located. Other than correlation correspondence method, the hierarchical coherence measurement is proposed to extract disparity maps quickly and densely from stereo image pairs. The coherence design assures precise measurement and the hierarchical design enables the phase-based disparity estimation to measure large disparities. The roadway surveillance system relies on implementing the roadway exploration mechanism and the median search architecture. The roadway exploration mechanism detects roadway pixels by the hue classification. This result is used to mask the disparity map so as to emerge objects out of the roadway. The median search architecture detects an obstacle as the region with disparity values larger than the median value and with size larger than noise patch. The regions attributed to obstacles are then located to generate three-dimension information for guidance and collision avoidance. The detailed design of the roadway surveillance system is presented. Experimental results show the feasibility and accuracy of the proposed design.
Subjects
立體視覺
視差
障礙物偵測
避碰
監控
Computational stereovision
disparity
obstacle detection
collision avoidance
surveillance
Type
thesis
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