Comparison of HOG, LBP and Haar-Like Features for On-Road Vehicle Detection

Ashwin Arunmozhi, Jungme Park
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引用次数: 26

Abstract

Autonomous vehicles may be the most significant innovation in transportation since automobiles were first invented. Environmental perception plays a pivotal role in the development of self-driving vehicles which need to navigate in a complex environment of static and dynamic objects. It is required to extract dynamic objects like vehicles and pedestrians more precisely and robustly to estimate the current position, motion and predict its future position. In this article, the performance of three commonly used object detection approaches, Histogram of Oriented Gradients (HOG), Haar-like features and Local Binary Pattern (LBP) is investigated and analyzed using a public dataset of camera images. The detection results show that for the same dataset, LBP features perform better than the other two feature types with a higher detection rate. Finally, a unique and robust detection algorithm using a combination of all the three different feature descriptors and AdaBoost cascade classification is proposed.
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HOG、LBP和Haar-Like特征在道路车辆检测中的比较
自汽车发明以来,自动驾驶汽车可能是交通领域最重要的创新。环境感知在自动驾驶汽车的发展中起着至关重要的作用,因为自动驾驶汽车需要在静态和动态物体的复杂环境中导航。需要更精确、鲁棒地提取车辆、行人等动态物体,以估计其当前位置、运动并预测其未来位置。在这篇文章中,研究了三种常用的目标检测方法,定向梯度直方图(HOG), haar样特征和局部二值模式(LBP)的性能,并使用相机图像的公共数据集进行了分析。检测结果表明,对于相同的数据集,LBP特征表现优于其他两种特征类型,具有更高的检测率。最后,提出了一种结合三种不同特征描述符和AdaBoost级联分类的独特鲁棒检测算法。
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