非平面道路上行人检测的三维候选选择方法

D. Fernández, I. Parra, M. Sotelo, P. Revenga, S. Alvarez, M. Gavilán
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引用次数: 15

摘要

提出了一种基于立体视觉的运动车辆行人检测方法。利用极极几何和鲁棒相关过程计算非密集三维地图。采用非平坦路面假设修正俯仰角变化。因此,非障碍点可以很容易地移除,因为它们位于道路上。采用自适应半径的减聚类算法在三维空间中选择一般障碍物。这种聚类技术可以针对不同类型的障碍物进行配置。本文提出了一种行人检测的优化配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D Candidate Selection Method for Pedestrian Detection on Non-Planar Roads
This paper describes a stereo-vision-based candidate selection method for pedestrian detection from a moving vehicle. Non-dense 3D maps are computed by using epipolar geometry and a robust correlation process. Non-flat road assumption is used for correcting pitch angle variations. Thus, non obstacle points can be easily removed since they lay on the road. Generic obstacles are selected by using Subtractive Clustering algorithm in a 3D space with an adaptive radius. This clustering technique can be configurable for different types of obstacles. An optimal configuration for pedestrian detection is presented in this work.
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