An FPGA Implementation of a HOG-based Object Detection Processor

Abstract

This paper describes a Histogram of Oriented Gradients (HOG)-based object detection processor. It features a simplified HOG algorithm with cell-based scanning and simultaneous Support Vector Machine (SVM) calculation, cell-based pipeline architecture, and parallelized modules. To evaluate the effectiveness of our approach, the proposed architecture is implemented onto a FPGA prototyping board. Results show that the proposed architecture can generate HOG features and detect objects with 40 MHz for SVGA resolution video (800 × 600 pixels) at 72 frames per second (fps).