Abstract

The problem of detecting objects in images is solved by training the classifier by the descriptors constructed based on the local Radon transform of the gradient field of the image. The space of the Radon transform is considered as the Hough space accumulator in which projections are constructed. The set of local projections forms a descriptor of the region related to the object that is a generalization of the known histogram of oriented gradients (HOG) descriptor. The issues of the effect of the approximation of the Radon transform contribution function, the form of local normalization, and the number of directions in the projection histograms on the results of detecting pedestrians are investigated. The results produced by the proposed descriptor are compared with the results obtained using the HOG descriptor and convolutional neural networks (CNN) based on the ResNext and MobileNet architectures on the INRIA and CityScapes databases.

中文翻译：

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在Radon变换空间中通过描述符训练分类器

摘要

通过基于基于图像梯度场的局部Radon变换构造的描述符训练分类器，解决了检测图像中对象的问题。Radon变换的空间被视为构建投影的霍夫空间累加器。局部投影的集合形成与对象有关的区域的描述符，该描述符是已知的定向梯度直方图（HOG）描述符的概括。研究了Radon变换贡献函数的逼近，局部归一化的形式以及投影直方图中方向数对行人检测结果的影响。