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This paper proposes a new framework for fast and reliable traffic sign detection using images obtained from a single front-facing road vehicle camera. Our focus is on a methodology for reducing the computational requirements and increasing the performance of existing detection methods by refining the image space search using 3D scene geometry. Information concerning physical traffic sign dimensions...
Scene reconstruction from motion and stereo are the two most popular methods for 3D range reconstruction. Because both techniques are affected by different types of errors, combining these two approaches to carry out 3D reconstruction seems natural. In this work, we present the initial results of our work towards the goal of analytically describing the performance of such a synergistic system. We...
To be able to determine the position of a static object in 3D space by means of computer vision, it has to be seen by cameras from at least two different view points. The same applies for measuring the position of a moving object based on images captured at one single time instant. However, if the cameras are not synchronized in time, or if a moving object is not visible in all images, one can not...
In this paper an approach for dynamic sensor selection in large video-based sensor networks for the purpose of multi-camera object tracking is presented. The sensor selection approach is based on computational geometry algorithms and is able to determine task-relevant cameras (camera cluster) by evaluation of geometrical attributes, given the last observed object position, the sensor configurations...
Geometric reconstruction problems in computer vision are often solved by minimizing a cost function that combines the reprojection errors in the 2D images. In this paper, we show that, for various geometric reconstruction problems, their reprojection error functions share a common and quasiconvex formulation. Based on the quasiconvexity, we present a novel quasiconvex optimization framework in which...
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