Introduction - If you have any usage issues, please Google them yourself
Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available