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 descr iptor vector

Dry-run the newly trained custom HOG descr iptor against training set and against camera images, if available-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 descr iptor vector

Dry-run the newly trained custom HOG descr iptor against training set and against camera images, if available