AdaBoost Engine

This paper presents an application specific engine dedicated for acceleration of AdaBoost image classifier. Ada-Boost and its modifications belong to the most successful algorithms of image classification. This class of algorithms can also be used for object detection through scanning of the image with a sliding window whose content is classified using AdaBoost. Such process, however, is very computationally demanding. The engine presented in this paper implements a novel feature extraction method, suitable specifically for hardware acceleration, whose classification performance is at the same time equal or better than performance of the more traditionally used features. The novel feature extraction is based on simultaneous processing of a small grid of picture elements that can be accessed from a memory in a single read operation. The architecture of engine utilizes principles of fine multithreading combined with pipelining. Preliminary tests of the engine on Xilinx Virtex II -250 show better results comparing to existing hardware implementations of image classification in accuracy, speed, and chip utilization.