Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

• 作者: Ching-Te Chiu Huai-Mao Weng
• 来源期刊: 交通科技期刊(英文)
• 学科: 工学
• 关键词: TRAFFIC SIGN Recognition Advanced Driver ASSISTANCE System REAL-TIME Processing Color Segmentation Connected Component Analysis Histo-gram of Oriented Gradient Support Vector Machine German TRAFFIC SIGN Detection BENCHMARK CMOS ASIC VLSI
• 年，卷（期）: 2018,（3）
• 页码范围: 209-231
• 页数: 23页
• 分类号: TP39

摘要

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.