HOG is a feature descriptor, so it belongs to the same family of algorithms as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and Oriented FAST and rotated BRIEF (ORB), which we covered in Chapter 6, Retrieving Images and Searching Using Image Descriptors. Like other feature descriptors, HOG is capable of delivering the type of information that is vital for feature matching, as well as for object detection and recognition. Most commonly, HOG is used for object detection. The algorithm – and, in particular, its use as a people detector – was popularized by Navneet Dalal and Bill Triggs in their paper Histograms of Oriented Gradients for Human Detection (INRIA, 2005), which is available online at https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf.
HOG's internal mechanism is really clever...