A system-on-chip FPGA design for real-time traffic signal recognition system

Traffic signal detection has long been an important function in an advanced driver assistance system (ADAS). This paper presents a complete system design based on the techniques of blob detection, histogram of oriented gradients (HOG) and support vector machine (SVM). Blob detection is applied to detect potential candidates, and then HOG and SVM is for feature classification. A novel hardware/software co-design architecture is developed for traffic light recognition at real-time. With well-balanced workload on FPGA fabric and the on-chip ARM processor, the entire system-on-chip can achieve a processing rate of 60 fps for XGA 1024-by-768 video. The system can achieve an accuracy rate of over 90% on both red lights and green lights. The proposed system can be improved by replacing HOG with more advanced feature algorithm to obtain higher accuracy.