我做过与您类似的事情：使用 HOG 收集正负图像样本以提取汽车特征，使用线性 SVM 训练特征集（我使用 SVM 光），然后使用模型使用 HOG 多检测功能检测汽车。
我得到很多误报，然后我使用正样本和误报+负样本重新训练数据。然后再次测试生成的模型。结果检测有所改善（误报率降低），但结果并不令人满意（平均 50% 的命中率和 50% 的误报率）。调整多检测参数可以改善结果，但效果不大（误报减少 10% 并提高命中率）。
编辑
如果你愿意，我可以分享你的源代码，我非常愿意讨论，因为我使用 HOG 没有得到令人满意的结果。无论如何，我认为代码可以作为使用 HOG 进行训练和检测的良好起点
编辑：添加代码
static void calculateFeaturesFromInput(const string& imageFilename, vector<float>& featureVector, HOGDescriptor& hog)
{
Mat imageData = imread(imageFilename, 1);
if (imageData.empty()) {
featureVector.clear();
printf("Error: HOG image '%s' is empty, features calculation skipped!\n", imageFilename.c_str());
return;
}
// Check for mismatching dimensions
if (imageData.cols != hog.winSize.width || imageData.rows != hog.winSize.height) {
featureVector.clear();
printf("Error: Image '%s' dimensions (%u x %u) do not match HOG window size (%u x %u)!\n", imageFilename.c_str(), imageData.cols, imageData.rows, hog.winSize.width, hog.winSize.height);
return;
}
vector<Point> locations;
hog.compute(imageData, featureVector, winStride, trainingPadding, locations);
imageData.release(); // Release the image again after features are extracted
}
...
int main(int argc, char** argv) {
// <editor-fold defaultstate="collapsed" desc="Init">
HOGDescriptor hog; // Use standard parameters here
hog.winSize.height = 128;
hog.winSize.width = 64;
// Get the files to train from somewhere
static vector<string> tesImages;
static vector<string> positiveTrainingImages;
static vector<string> negativeTrainingImages;
static vector<string> validExtensions;
validExtensions.push_back("jpg");
validExtensions.push_back("png");
validExtensions.push_back("ppm");
validExtensions.push_back("pgm");
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Read image files">
getFilesInDirectory(posSamplesDir, positiveTrainingImages, validExtensions);
getFilesInDirectory(negSamplesDir, negativeTrainingImages, validExtensions);
/// Retrieve the descriptor vectors from the samples
unsigned long overallSamples = positiveTrainingImages.size() + negativeTrainingImages.size();
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Calculate HOG features and save to file">
// Make sure there are actually samples to train
if (overallSamples == 0) {
printf("No training sample files found, nothing to do!\n");
return EXIT_SUCCESS;
}
/// @WARNING: This is really important, some libraries (e.g. ROS) seems to set the system locale which takes decimal commata instead of points which causes the file input parsing to fail
setlocale(LC_ALL, "C"); // Do not use the system locale
setlocale(LC_NUMERIC,"C");
setlocale(LC_ALL, "POSIX");
printf("Reading files, generating HOG features and save them to file '%s':\n", featuresFile.c_str());
float percent;
/**
* Save the calculated descriptor vectors to a file in a format that can be used by SVMlight for training
* @NOTE: If you split these steps into separate steps:
* 1. calculating features into memory (e.g. into a cv::Mat or vector< vector<float> >),
* 2. saving features to file / directly inject from memory to machine learning algorithm,
* the program may consume a considerable amount of main memory
*/
fstream File;
File.open(featuresFile.c_str(), ios::out);
if (File.good() && File.is_open()) {
File << "# Use this file to train, e.g. SVMlight by issuing $ svm_learn -i 1 -a weights.txt " << featuresFile.c_str() << endl; // Remove this line for libsvm which does not support comments
// Iterate over sample images
for (unsigned long currentFile = 0; currentFile < overallSamples; ++currentFile) {
storeCursor();
vector<float> featureVector;
// Get positive or negative sample image file path
const string currentImageFile = (currentFile < positiveTrainingImages.size() ? positiveTrainingImages.at(currentFile) : negativeTrainingImages.at(currentFile - positiveTrainingImages.size()));
// Output progress
if ( (currentFile+1) % 10 == 0 || (currentFile+1) == overallSamples ) {
percent = ((currentFile+1) * 100 / overallSamples);
printf("%5lu (%3.0f%%):\tFile '%s'", (currentFile+1), percent, currentImageFile.c_str());
fflush(stdout);
resetCursor();
}
// Calculate feature vector from current image file
calculateFeaturesFromInput(currentImageFile, featureVector, hog);
if (!featureVector.empty()) {
/* Put positive or negative sample class to file,
* true=positive, false=negative,
* and convert positive class to +1 and negative class to -1 for SVMlight
*/
File << ((currentFile < positiveTrainingImages.size()) ? "+1" : "-1");
// Save feature vector components
for (unsigned int feature = 0; feature < featureVector.size(); ++feature) {
File << " " << (feature + 1) << ":" << featureVector.at(feature);
}
File << endl;
}
}
printf("\n");
File.flush();
File.close();
} else {
printf("Error opening file '%s'!\n", featuresFile.c_str());
return EXIT_FAILURE;
}
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Pass features to machine learning algorithm">
/// Read in and train the calculated feature vectors
printf("Calling SVMlight\n");
SVMlight::getInstance()->read_problem(const_cast<char*> (featuresFile.c_str()));
SVMlight::getInstance()->train(); // Call the core libsvm training procedure
printf("Training done, saving model file!\n");
SVMlight::getInstance()->saveModelToFile(svmModelFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Generate single detecting feature vector from calculated SVM support vectors and SVM model">
printf("Generating representative single HOG feature vector using svmlight!\n");
vector<float> descriptorVector;
vector<unsigned int> descriptorVectorIndices;
// Generate a single detecting feature vector (v1 | b) from the trained support vectors, for use e.g. with the HOG algorithm
SVMlight::getInstance()->getSingleDetectingVector(descriptorVector, descriptorVectorIndices);
// And save the precious to file system
saveDescriptorVectorToFile(descriptorVector, descriptorVectorIndices, descriptorVectorFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Test detecting vector">
cout << "Test Detecting Vector" << endl;
hog.setSVMDetector(descriptorVector); // Set our custom detecting vector
cout << "descriptorVector size: " << sizeof(descriptorVector) << endl;
getFilesInDirectory(tesSamplesDir, tesImages, validExtensions);
namedWindow("Test Detector", 1);
for( size_t it = 0; it < tesImages.size(); it++ )
{
cout << "Process image " << tesImages[it] << endl;
Mat image = imread( tesImages[it], 1 );
detectAndDrawObjects(image, hog);
for(;;)
{
int c = waitKey();
if( (char)c == 'n')
break;
else if( (char)c == '\x1b' )
exit(0);
}
}
// </editor-fold>
return EXIT_SUCCESS;
}
