HOG特征描述符训练如何进行？
openCV 中似乎没有任何 HOG 训练的实现，并且关于 HOG 训练如何工作的资料也很少。据我所知，HOG 训练可以实时完成。但培训有什么要求呢？培训过程实际上是如何进行的？
There doesn't seem to be any implementations of HOG training in openCV and little sources about how HOG training works. From what I gathered, HOG training can be done in real time. But what are the requirements of training? How does the training process actually work?
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与大多数计算机视觉算法一样，Google 学术搜索是你的朋友:)我建议阅读一些关于它如何工作的论文。 这里是一个供您入手的关于 HoG 的参考文献最多的论文。
研究计算机视觉时的另一个技巧是记下您感兴趣的论文的作者，并尝试找到他们的网站。他们往往会拥有算法的实现以及如何使用它们的经验规则。另外，请查找论文中有关您的算法的参考文献。这对于获取背景知识以真正理解算法的工作原理和原因非常有帮助。
As with most computer vision algorithms, Google Scholar is your friend :) I would suggest reading a few papers on how it works. Here is one of the most referenced papers on HoG for you to start with.
Another tip when researching in computer vision is to note the authors of the papers you find interesting, and try to find their websites. They will tend to have an implementation of their algorithms as well as rules of thumb on how to use them. Also, look up the references that are sited in the paper about your algorithm. This can be very helpful in aquiring the background knowledge to truly understand how the algorithm works and why.
你的术语有点混乱。 HOG是一个特征描述符。您可以使用 HOG 训练分类器，该分类器又可用于对象检测。 OpenCV 包括一个使用 HOG 特征的人体检测器和一个 SVM 分类器。它还包括 CascadeClassifier，可以使用 HOG，通常用于人脸检测。
OpenCV中有一个名为opencv_traincascade的程序，它可以让你训练级联目标检测器，它让您可以选择使用 HOG。 MATLAB 计算机视觉系统工具箱中有一个函数，名为 trainCascadeObjectDetector，它做同样的事情。
Your terminology is a bit mixed up. HOG is a feature descriptor. You can train a classifier using HOG, which can in turn be used for object detection. OpenCV includes a people detector that uses HOG features and an SVM classifier. It also includes CascadeClassifier, which can use HOG, and which is typically used for face detection.
There is a program in OpenCV called opencv_traincascade, which lets you train a cascade object detector, an which gives you the option to use HOG. There is a function in the Computer Vision System Toolbox for MATLAB called trainCascadeObjectDetector, which does the same thing.