Intelligent visual analysis algorithms are used more and more widely in different areas, such as traffic control, security monitoring, entertainment areas and so on. With the increase in the amount of data, intelligent visual analysis algorithms cannot run in real time in the face of huge amounts of data. This makes systems unable to be integrated with more functions, limiting applications to few scenarios. Although CUDA (Compute Unified Device Architecture) only rises in the recent years, it has gained rapid development and lots of applications, especially in the entertainment and medicine areas. For the low price of GPUs, many desktop personal computers have installed GPU. For the reasons above, the paper focuses on how to use CUDA to accelerate intelligent visual analysis algorithms. And the results are applied in practical projects. The main work and contributions are listed below: The characteristics of the CUDA technology and intelligent visual analysis algorithms are analyzed in detail. On the basis, the framework of using CUDA technology and several general accelerating approaches are summarized. The summarized general approaches are used to accelerate Gaussian mixture model. The accelerated algorithm is as 30 times faster as the original one. The effect has proved the validity of the summarized general approaches. The summarized general approaches are used to accelerate HLSIFT, achieving the speedup of more than 20 times. The good results verify the validity of the summarized general approaches. The accelerated HLSIFT algorithm is used in the bank virtual automatic teller machine project. It does not only prove the validity of CUDA technology in practical, but also extends the application field of intelligent visual analysis algorithms. In addition, it provides a new way of thinking and a solution for the bank card password protection. The paper does not only make some progress in the research, but also applies research results in practical projects.
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