| Diversity and complexity of surveillance environment leads to poor robustness for video analysis algorithms. Complex algorithms can not meet real-time requirement under embedded environment. In order to make video analysis algorithms suit for embedded environment, this thesis mainly researches on video analysis algorithms, algorithms implementation and optimization,and application interface of video analysis under embedded environment.The scarcity of resources under embedded environment determines the video analysis algorithms must be simple, efficient and robust. This thesis makes a research on background subtraction, frame difference and optical flow for moving object detection algorithm. Through theoretical analysis and experimental simulation, background subtraction is selected as moving object detection algorithm. An improved locating algorithm for the four boundaries of a object is proposed on the basis of pixel-labeled algorithm by changing the direction of image scanning.Compared to region growing algorithm and pixel-labeled algorithm, the improved algorithm has a lower temporal and spatially complexity. Mean Shift algorithm combining with Kalman filter provides an effective solution to tracking of fast moving objects and objects with large proportion of occlusion. Then, a moving object detection software is designed and implemented under embedded environment. Through code optimization, the software meets real-time requirement. By researching on video analysis application structure, video analysis configuration interface and scene description interface of ONVIF core specification,video analysis application interface is designed and implemented under embedded environment. A technical solution is proposed for video stream synchronization between network video processor and video analysis processor.It provides the ability of configurable software and interoperability between network video processor and video analysis processor. |
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