An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection

Abstract

In intelligent transportation systems, human detection is an important issue and has been widely used in many applications. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection. In this paper, we present a low-cost high-speed hardware implementation for HOG feature extraction. The simulation shows that the proposed circuit can achieve 167 MHz with 153-K gate counts by using Taiwan Semiconductor Manufacturing Company 0.13-μm technology. Compared with the previous hardware architectures for HOG feature extraction, our circuit requires fewer hardware costs and achieves faster working speed.