复杂环境下的取走物检测-计算数学专业论文.docxVIP

复杂环境下的取走物检测 REMOVED OBJECT DETECTION UNDER COMPLEX ENVIRONMENTS ABSTRACT Removed Object detection is one of the important aspects of Intelligent Video Surveillance, which combines the advanced technologies of image processing, video analysis, artificial intelligence, pattern recognition and so on. It both can be applied to museums, exhibition halls and other public places and also be for key cultural sites, ordnance powerhouse and other privacy units. This thesis mainly studies the removed object detection based on static monocular camera, aims to detect one or more targets in the region of interest (ROI), once the target has been removed and the time of the target disappearance beyond the preset time, it immediately start-up a alarm. The main works are: Firstly, we use interactive way to get the ROI of the target to be detected. The setting of ROI is one to one, we can monitor one or more target at the same time. Under demands, the users can modify, add and remove a ROI real-time. Secondly, we uniting the advantages of Gaussian mixture background model and the time-mean background model , mainly based on the Gaus- sian mixture background model, within the time when there is light muta- tion ,we use time-mean background model to detect the target, and real- time update the Gaussian mixture background model, when the model is stable, we use the Gaussian mixture background model again for foreground detection. Thirdly, based on the difference of scene complexity, we proposed two methods for distinguish the event of target occluded and removal: First, based on brightness information, we use the pixel’s mean value, this method is simple, high efficiency and suitable for simple scene. Second, we use the seed filling algorithm based on color information, experiments show that this method is suitable for complexity scene and have better stability. ii 原创性声明 本人郑罩声明 :所呈交的学位论文，是本人在导师指导下 ，进行研究工作所取得的成 果。除文中己经注明引用的内容外，本学位论文不包含任何他人或集体己经发表的作品内 容，也不包含本人为获得其他学位而使用过的材料。对本论文所涉及的研究工作