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Exploiting a Scene Calibration Mechanism for Depth Estimation

机译:利用场景校准机制进行深度估计

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The problem of depth estimation from one or more image(s) Is most frequently discussed In computer vision using binocular cues, motion parallax or monocular cues. In this paper, we exploited a scene calibration mechanism for estimating depth from a single image, with emphasis on motorways. The approach incorporates linear perspective depth cue to restore distance information of vehicle(s) from a given image. Based upon the assumption that linear perspective is available in ample amount in structured environments, proposed approach computes 1D projective transformation across ground plane which maps imaged distances to the corresponding real-world distances. Once the homography matrix for 1D projective transformation is available, it can be applied to any point to compute its straight line distance from the reference point. Experimental results show that the proposed approach is computationally efficient and delivers desirably accurate depth estimates; thus, it has been applied to identify over-speedings.
机译:使用双目提示,运动视差或单眼提示,在计算机视觉中最常讨论一个或多个图像的深度估计问题。在本文中,我们利用了一种用于估计从单个图像的深度的场景校准机制,重点是高速公路。该方法包括线性透视深度提示,以从给定图像恢复车辆的距离信息。基于线性透视图在结构环境中的充足量中可用的假设,所提出的方法计算跨接地平面的1D投影转换,该平面将成像距离映射到相应的现实世界距离。一旦提供了1D投影转换的定址矩阵,它可以应用于任何点以计算与参考点的直线距离。实验结果表明,该方法是计算上有效的,并提供理想的精确深度估计;因此,它已被应用于识别过超速度。

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