Research Of The Moving Object Detection And Tracking Based On Background Modeling

Moving object detection and tracking is the basis of intelligent visualsurveillance, and has a wide application prospect in the field of traffic, military,industry, biomedical and so on. Among them, the detection of moving target instatic scene Is the most widely used.and it is a hot research topic. At present, themost common method of moving target detection in static scene is backgroundsubtraction.The main problems is the background extraction and updating.Acomplex background model for a accurate detection always leads to large amount ofcalculation.It is not conducive to real-time detection.A simple model is always notaccurate enough and is not conducive to the accuracy of target detection.In addition,because of the real-time detection, we also need to consider the memory space oftemplates.Based on the existing research results, and aimed at the main problem ofmoving target detection and tracking in static scene, this paper makes in-depthresearch.The main work is as follow.Firstly,on the research of moving detection:a great number of existing movingobject detection methods are introduced in this paper,including optical flow fieldmethod, temporal difference method, as well as background subtraction method. Asto background subtraction method,Classic algorithms,such as the multi-frameaveraging method, the Gaussian Mixture Model(GMM) method and the codebookmethod are described in detail.Because the GMM method is time-consuming andthe codebook method nedds a larger memory space, A background modeling andobject detection method based on the clustering of pixel values is presented in thispaper.During the establishment of the background, the template substitutionmechanism is used to save memory space.A sentence about weather the backgroundis completed is used to save time.Then,we use morphological filter to filter out thenoise and fill the internal hollow. Finally,the object can be segmented by theintegral projection method.Experiments show that, the algorithm can be a verygood detection of moving targets in different scenarios. The speed and the accuracyof the algorithm has improved to some extent.Secondly,on the research of object tracking,by studying several commonobject tracking algorithm, an improved Mean Shift algorithm based on Kalmanprediction is presented.It takes the object’s histogram as the feature mask.We use the Kalman Filter to predict the location in next frame to reduce the searchingtime.Then the Mean Shift algorithm is used to search and match for movingobject.With the introduction of information measure,the tracking template can bechanged adaptive, it can be a good tracking even when the size of the moving targetis changed.