The Implementation of Support Vector Machine (SVM) using FPGA for Human Detection

Computing time, Low power consumption and low cost are advantages of implementation machine learning based on embedded hardware. Variety applications are applied embedded machine learning system such as Driver assistance system, Robotic system, Video surveillance application and other. FPGA is one of popular hardware device that lots researches select to construct machine learning application because of low power running and high-speed computation and parallelism.

Human or pedestrian detection is an attractive headline and has been proposed in computer vision and machine learning fields. Real time detection and low power system is a critical challenges. Support Vector Machine algorithm with Histograms of oriented gradients (HOG) feature descriptor is given a high successful result, fast and reliable, for human detection. Therefore, this paper demonstrates how to implement HOG feature descriptor with Support Vector Machine (SVM) using FPGA and presents a report that includes FPGA's resource utilization, time consuming, power consumption and SVM accuracy results.