Design and Implementation of a Field Programmable Gate Array-Based Pedestrian Detection Framework for Autonomous...

Autonomous driving offers a promising way to tackle the rising number of fatalities from traffic accidents. An autonomous vehicle includes many features, but the ability to detect pedestrians is crucial, challenging, and relevant to various real-time situations like surveillance, tracking people, and monitoring. Accurately identifying pedestrians is difficult because they can appear in different shapes, positions, and postures. They can wear various types of clothing and sometimes be partially hidden or blend in with nearby objects. This paper focuses on the real-time detection of pedestrians for self-driving cars using a popular hardware platform: The field programmable gate array (FPGA), Ultra 96 v2. The study implements a method for pedestrian detection based on a histogram of oriented gradients (HOG) combined with a support vector machine (SVM) classifier to recognize individuals on the FPGA board, leveraging high-level synthesis (HLS) tools. The effectiveness of the system has been tested on both still images and live video. The results show that advanced FPGA boards like the Ultra 96 v2 significantly improve performance metrics. The system operates at a clock frequency of 150 MHz while using less than half of the available resources and consuming around 2.5 W of power. Also, the system reports the pedestrian detection accuracy close to 95% and other efficient metrics for detection evaluation, like precision (78.6%), recall (88.3%), and F1 Score (83.1%). In summary, the developed system can detect pedestrians in real-time and has the potential to significantly improve the development of a smart and safe transportation environment.