Vehicle detection using PLS Hough transform

R. Takeuchi, K. Kato, David Harwood, L. Davis
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引用次数: 6

Abstract

This paper proposes extended Generalized Hough Transform (GHT) to introduce training process by using Partial Least Squares (PLS) regression analysis. Hough transform can robustly detect patterns against noise and occlusions, and GHT is adapted to perform the generic object detection. In this study, we introduced training process to determine the voting weight of GHT by using PLS regression analysis. Thereby, it becomes possible to generic object detection, while maintaining the framework of Hough-based object detection. In this paper, we applied PLS Hough transform to the vehicle detection from satellite images. In addition, we compared PLS Hough transform with the previous approach (original GHT) on the vehicle detection, and our proposed method achieved high detection accuracy.
基于PLS霍夫变换的车辆检测
本文提出扩展广义霍夫变换(GHT),利用偏最小二乘(PLS)回归分析引入训练过程。霍夫变换可以鲁棒地检测噪声和遮挡下的模式,而GHT适用于执行通用目标检测。在本研究中,我们引入训练过程,使用PLS回归分析来确定GHT的投票权重。从而使通用目标检测成为可能,同时保持基于hough的目标检测框架。本文将PLS霍夫变换应用于卫星图像的车辆检测。此外,我们将PLS霍夫变换与之前的方法(原始GHT)在车辆检测上进行了比较,我们提出的方法取得了较高的检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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