Hardware implementation of an improved HOG descriptor for pedestrian detection

Abstract

Recently, pedestrian detection system has become an important issue to improve the road safety through its integration in several Advanced Driver Assistance Systems (ADAS). According to the literature, the use of the Histogram of Oriented Gradients (HOG) descriptor has been very efficient and robust in the development of such systems. However, the intensive calculation steps of this descriptor make its implementation an issue to be address in order to meet real-time requirement. In this paper, an efficient hardware architecture based on an improved HOG version and linear SVM (Support Vector Machine) classifier is emphasized. The proposed architecture has founded on cell-based scanning, some approximations and without any pixelbuffers. The system evaluation was carried out on a Xilinx Zynq platform. Experimental results showed that our approach can achieve an efficient pedestrian detection for full HD (High-Definition) video with a reduced hardware resource and power consumption.