Object detection is one of the modern challenges in computer vision. There are many ways to detect classes of objects from static images, and most of them are based on the Histogram of Oriented Gradients (HOG) algorithm. Pixels have sharp variations at the boundary of objects, and this is exploited in HOG, where pixels’ gradients are computed to highlight such boundaries. The first step in HOG based object detection is therefore to generate a ”Feature Map”, a version of the image comprised of gradient vectors that allow to proceed with the actual object detection. The ability to process videos as well as images with HOG opens many possibilities in terms of possible applications, such as object recognition for cars, robots and so on; security footage; smart search to find sequences of interest in movies, just to name a few.