Apply Modern Image Recognition Techniques with CUDA Implementation on Autonomous Systems

Abstract

Computer vision has been developed rapidly in the last few decades and it has been used in a variety of fields such as robotics, autonomous vehicles, traffic surveillance camera etc. nowadays. However, when we process these high-resolution raw materials from the cameras, it brings a heavy burden to the processors. Because of the physical architecture of the CPU, the pixels of the input image should be processed sequentially. So even if the computation capability of modern CPUs is increasing, it is still unable to make a decent performance in repeating one single work millions of times. The objective of this thesis is to give an alternative solution to speed up the execution time of processing images through integrating popular image recognition algorithms (SURF and FREAK) on GPUs with the help of CUDA platform developed by NVIDIA, to speed up the recognition time. The experiments were made to compare the performances between traditional CPU-only program and CUDA program, and the result show the algorithms running on CUDA platform have a significant speedup.