Research And Design Of Video Server Based On ARM

With the rapid development of VLSI(Very Large Scale Integrated) circuits,computer network technology,image processing technology and the rise of artificial intelligence discipline, embedded intelligent video server system based on ARM has become a new research direction in the field of video surveillance system, with higher value of technology applications and theoretical research significanceIn order to support the general-purpose USB camera and solve the problem of inefficient image processing block in the traditional video server systems,this paper provides a embedded video server application using USB camera, the method of transplanting drivers of USB camera is introduced,the programming language for image acquisition based on V4L is elaborated. Finally an embedded intelligent video server system application based on ARM is proposed in this paper, which combined with computer vision Library (OpenCV) efficient algorithm.For the practical application of digital network video server as the research object, with the ARM9-based microprocessor and Linux operating system as the core technology, a kind of real-time streaming video server based on embedded technology is designed The main topics of this paper's work is at two levels from the hardware design and software design of embedded video server, and with a moving target vehicle detection algorithm as an example, to justify the feasibility and effectiveness of the streaming video server design. The transplant of USB camera on is the most important research on hardware design, and the software design focus on the use of open source Computer Vision Library (OpenCV) efficient algorithms on this video surveillance system.Meanwhile, as a further exploration, this technology is introduced in intelligent transportation system (ITS) areas, with the expectation of the advanced video server not only can complete the traditional video collection and network sending functions, but also complete the moving detection and traffic flow or vehicle speed detection and other traffic stream parameters acquisition for the intelligent transportation systems in order to response rapidly and provide a reasonable schedule information.