Algorithms Study On Computer Intelligent Video Surveillance System And A New Background Difference Model

Computer intelligent video surveillance system(CIVSS) is one of the new arising high-tech application fields. It includes knowledge of computer science application mathematics,pattern analysis,image engineering,etc. CIVSS can automatically analysis image sequence with the methods of computer vision and video analysis. The system can real-time detect, recognize,and track moving objects in a special environment. Furthermore,it can also analysis and judge the behavior of objects. Now it is used in monitoring, fire monitoring,traffic flow and peccancy, and the surveillance for bank, shopping, parking lots aerodrome, underground, etc.As intelligent video monitoring has such broad application , so we go deep into the subject .We have solved the problems caused by the shadow and the obstruction ,so we can accurate segment objects . we expend the premise,and now in our algorithm it is not necessary to demand that there is no active objects in the training time . we also implement our algorithm with computer procedure ,and get a satisfied result .In chapter 1 we introduce some of the results such as the VSAN system of the United States Military,the ADVISOR system developed by EU,the W~4 systems developed by Haritaoglu,etc .Based on these successful system ,we divide the develop flow into 4 stages : stage 1 Image data acquisition , stage 2 change detection , stage 3 target classification , stage 4 behavior understanding .We introduce the main work we have to do in each stage . And the most important part is the change detection stage . In the second chapter we summarizes the classical algorithm of the gray image CIVSS.It has two models:one is Non-difference background model,the other is difference background model.One of the non-difference background is Optical Flow method.There is assumptions that the value of the image don't change a lot during a short period of time.Use this assumptions ,we can make a basic equation of optical flow,then compute the field of optical flow.This method can find out the object when the camera is moving .However,this method is complex and sensitive to noise.If it is used in CIVSS,some special equipments are needed in the system .Difference background model is used when the camera is fitted.The main idea is:first we established background model, and then judge changes between the current frame and background model,find out the moving objects.Background subtraction ,frame difference,W~4,mixed Guassian ,kalman filter are always used in the difference background model .The mixed guassian used a number of Guass model to establishes background.The method can be used in multi-modal circumstances of motion detection. However, due to the establishment of a number of Gaussian models, the parameters of the background can't not be updated correctly.So this method can't be used in real-time system.The method of background subtraction and W~4 need a period of time for training .During this period of time,they choose the best background .This kind of algorithm is simple and fast. But if the object exist during the training time, there will be error in the system .The gray image CIVSS can't find out the false part,like shadow ,which is made by foreground objects. And the same problem is also exists in linear RGB color space . The third chapter presents a new non-linear color model. RGB component will be converted into color components and brightness components.To simplify this new color model,we introduce the concept of Allowed Changing Cone, by the judgement of if pixel is in the Allowed Changing Cone, and determine future goals and greatly reduce the amount of computations. In the third chapter, in the background substraction, W~4 methods, if there exist foreground in the training phase , it will detect ghost phenomenon, the article presents a template through the establishment of two temporary and, in conjunction with frame difference, we determined the changes in the new campaign algorithm. The method combines the two advantages of background and frame difference.Remove the false part through Bool operation of the two templates.The algorithm is simple and easy to realize .We developed the color image intelligent video monitoring system by ourselves .In the third chapter, more details about our system are involved in the changing detection stage process,image pre-processing and post-processing, background updates, and has done introduced.In the fourth chapter,we summarizes some image features widely used in object classification .For example:area, perimeter,gravity and so on.But these features aren't nature,because they can change with position of foreground.In this condition we prefer the concept of foreground contour , the nature of this feature is more comparison. And also we introduce a method that active contour to find out the foreground contour.In chapter 5 we showed the experimental results of our system .