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Three-dimensional vehicle pose estimation from two-dimensional monocular camera image for vehicle classification

机译:从二维单目摄像机图像中进行三维车辆姿态估计以进行车辆分类

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摘要

In this paper, a new method is proposed to estimate the pose of a moving vehicle in a typical traffic scene. Pose determination is crucial in the process of fitting or matching an existing 3D model in the database with the captured moving object. In this work, pose estimation is determined by estimating the 3D position of the moving vehicle in world space and the n computing the motion vector of the vehicle. The pose estimation is first initialized by loosely calibrating the camera view port and the perspective distortion. The 3D position is then determined by computing the intersection of a ray trace of the vehicle ’s centroid obtained from the video image originating from the camera eye to the vehicle ’s motion plane, i .e. the ground. Once a motion vector is obtained, the 3D model is aligned to match the vehicle’s pose. The computation is performed on a 3D graphics card. Results on real -world traffic scenes as well as synthetic data are presented and several issues are outlined.
机译:本文提出了一种新的方法来估计典型交通场景中移动车辆的姿态。在将数据库中现有的3D模型与捕获的运动对象进行拟合或匹配的过程中,姿势确定至关重要。在这项工作中,姿态估算是通过估算世界空间中移动车辆的3D位置并计算车辆的运动矢量来确定的。首先通过宽松地校准摄像机视口和透视图失真来初始化姿势估计。然后,通过计算从质心从摄像机眼睛到车辆运动平面的视频图像获得的车辆质心的光线轨迹的交点,可以确定3D位置。地面。一旦获得运动矢量,就将3D模型对齐以匹配车辆的姿势。该计算在3D图形卡上执行。给出了现实世界交通场景的结果以及综合数据,并概述了一些问题。

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