Traffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has... Show moreTraffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has been investigated vigorously in the last decade. It is an important step for introducing intelligent vehicles into the current road transportation systems. Based on image processing and machine learning technologies, TSR systems are being developed cautiously by many manufacturers and have been set up on vehicles as part of a driving assistant system in recent years. Traffic signs are designed and placed in locations to be easily identified from its surroundings by human eyes. Hence, an intelligent system that can identify these signs as good as a human, needs to address a lot of challenges. Here, ―good‖ can be interpreted as accurate and fast. Therefore, developing a reliable, real-time and robust TSR system is the main motivation for this dissertation. Multiple TSR system approaches based on computer vision and machine learning technologies are introduced and they are implemented on different hardware platforms. Proposed TSR algorithms are comprised of two parts: sign detection based on color and shape analysis and sign classification based on machine learning technologies including nearest neighbor search, support vector machine and deep neural networks. Target hardware platforms include Xilinx ZedBoard FPGA and NVIDIA Jetson TX1 that provides GPU acceleration. Overall, based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds on the GPU board. Ph.D. in Computer Engineering, December 2016 Show less
