Abstract
This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.
Highlights
In the field of vehicle navigation, stereo vision has mainly been applied to a large variety of applications, such as obstacle detection and tracking [1, 2], traffic sign detection and recognition [3,4,5,6], pedestrian detection and tracking [7], and so on
Zhang et al [31] use spatial and temporal information by extending the spatial window to a spatio temporal window, spatial window used to compute the sum of squared di↵erence (SSD) cost function, spatio temporal window to compute the sum of SSD (SSSD)
The first one is the geometric constraint that defines the minimum disparity threshold, resulting from the sensor geometry, which assumes that a pair of edge points eiL and eRj appearing in the left and right image lines, respectively, represent possible match only if the constraint xiL > xRj is satisfied [41], xiL and xRj are the x-coordinate of eiL and eRj, respectively
Summary
In the field of vehicle navigation, stereo vision has mainly been applied to a large variety of applications, such as obstacle detection and tracking [1, 2], traffic sign detection and recognition [3,4,5,6], pedestrian detection and tracking [7], and so on. A taxonomy of dense disparity estimation algorithms together with a testbed for quantitative evaluation of stereo algorithms is provided by Scharstein and Szeliski [16] It was demonstrated from [16] that Graph cuts methods [17,18,19,20] produce good results. They are time consuming, In order to avoid this problem in this work, the edges of the stereo images are extracted to reconstruct the scene. The estimated disparity ranges reduce the possible matches, which discard the false candidates and improve the results They reduce the execution time of the applied energy minimization approach
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