Moving Object Detection Based On Opencv And Its Dsp Technology Research

With the development of computer network, image processing and transmission, the technology of intelligent video surveillance has also rapid development in recent years. In the intelligent video surveillance system, moving object detection is basic and key part. The result of moving object extraction directly affects the following treatment and the effect of video surveillance.Background subtraction is an important method of motion detection, which is key and difficulty obtain background images and real time updating. Based on related data, the paper presents a new background modeling algorithm for detecting foreground objects. According to the hypothesis that the background pixel appears in image sequences with maximum frequency, the algorithm is a new background modeling algorithm based on pixel frequency, in which the pixels of the image are classified according their frequency and select the category with the largest number of pixels as the background that is the average value of these pixels in the category. A real time and regularly updating methods are used to adapt the background variations in this algorithm. The algorithm is implement base on OpenCV library. Experimental results show that the algorithm can draw out the background from the image with some moving foregrounds fast and robustly, which can adapt the gradual and sudden illumination changes. Therefore, the new algorithm can be used in real-time video surveillance system for effectively detecting moving targets.The ultimate goal of the research on moving object detection algorithm is applied to a practical video surveillance and monitoring system. In order to achieve practical application of the algorithm and verify the practicality of the proposed algorithm, the paper plans to transplant the algorithms on OpenCV to the platform of ADSP. In this paper, the simulation and analysis of the transplantation are described the completion of the work to be done to further study.