为了使用opencv HOG描述符和SVM分类器检测任意对象，您需要先训练分类器。使用参数在这里不会有帮助，对不起。

广义上，您需要完成以下步骤：

步骤1）准备要检测的对象的一些训练图像（正样本）。

步骤2）检测训练样本的HOG特征并使用此功能训练SVM分类器（也在OpenCV中提供）。

步骤3）使用训练的SVM分类器的系数HOGDescriptor :: setSVMDetector（）方法。

只有这样，您才能使用peopledetector.cpp示例代码来检测要检测的对象。

I have a project, which I want to detect objects in the images; my aim is to use HOG features. By using OpenCV SVM implementation , I could find the code for detecting people, and I read some papers about tuning the parameters in order to detect object instead of people. Unfortunately, I couldn't do that for a few reasons; first of all, I am probably tuning the parameters incorrectly, second of all, I am not a good programmer in C++ but I have to do it with C++/OpenCV... here you can find the code for detecting HOG features for people by using C++/OpenCV.

Let's say that I want to detect the object in this image. Now, I will show you what I have tried to change in the code but it didn't work out with me.

The code that I tried to change:

HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());

I tried to change getDefaultPeopleDetector() with the following parameters, but it didn't work:

(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9, 0,-1, 0, 0.2, true, cv::HOGDescriptor::DEFAULT_NLEVELS)

I then tried to make a vector, but when I wanted to print the results, it seems to be empty.

vector<float> detector;

HOGDescriptor hog(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9, 0,-1, 0, 0.2, true, cv::HOGDescriptor::DEFAULT_NLEVELS);

hog.setSVMDetector(detector);

Please, I need help solving this problem.

In order to detect arbitrary objects with using opencv HOG descriptors and SVM classifier, you need to first train the classifier. Playing with the parameters will not help here, sorry :( .

In broad terms, you will need to complete the following steps:

Step 1) Prepare some training images of the objects you want to detect (positive samples). Also you will need to prepare some images with no objects of interest (negative samples).

Step 2) Detect HOG features of the training sample and use this features to train an SVM classifier (also provided in OpenCV).

Step 3) Use the coefficients of the trained SVM classifier in HOGDescriptor::setSVMDetector() method.

Only then, you can use the peopledetector.cpp sample code, to detect the objects you want to detect.