Research On Background Modeling And Moving Object Detection Based On Serial Images

The detection and tracking moving targets in serial images are not only the main research field in computer vision and image coding,but also one of the most challenging research field in computer vision recently. It has vast applications in automation navigation, video surveillance and monitoring, medicine image analysis and video image compression and transmission. The research of the paper is focused on the serial image based background modeling and object detection.The paper introduces common approaches and algorithms of object detection home and abroad including the difference method, background modeling method and optical flow method, and then analysis the applicable field, advantages and disadvantages for each algorithm. Since difference based background modeling works poorly when the object speed is changeable, symmetrical difference method based background modeling is proposed. The symmetrical difference can quickly detect the object shape, gain more reliable background model and is more adaptable to the changeable environment.The paper has also proposed a new object detection method to get higher detection correctness, adopting symmetrical difference method into object detection. It totally covers the object detection based on difference, and also efficiently detects those areas in which it works poorly. Efficient and good segmentation performance is demonstrated by the experiment results. At the same time, the paper makes the research work more practical for it adopts the standard Hall serial image for experiments and algorithm analysis.