A Method Of Moving Objects Detection And Tacking For Video Image

Moving objects detection and tracking in video sequences is a task related to video image processing, computer vision, pattern recognition, artificial intelligence and other areas. It is an important research topic all the time in recent several years because of its widely application in business and military fields. However, due to the inherent complexity of the video image, it is still a challenging task for proposing a robust and accurate algorithm for moving objects detection and tracking.This paper focuses on moving objects detection and tracking technique under static background. For the moving objects detection, firstly, the detection methods at present are classified and summarized, and their advantages and disadvantages are analyzed. Then, the objects detection methods based on Gaussian model are analyzed and discussed in detail, and a new algorithm is proposed, in which the mean and variance of the model are used at different update rates. The problem of the variance slow convergence in traditional algorithm is solved and the stability of Gaussian model is increased by the algorithm that uses the number of model match to decide update coefficient of variance. Because traditional Gaussian mixture model only builds up the model for a pixel in video image and neglects the spatial local correlation, the accuracy of moving objects detection is not high. In order to solve this problem, a detection method based on spatial local correlation is proposed. The algorithm combines the spatial local correlation with mixture model by redefining energy function of the Markov random field, and then an adaptive threshold is obtained for moving objects detection. The experimental results show that the proposed method can better adapt to the dynamic scenes, and obtain a more accurate objects detection result.For the moving objects tracking, in order to improve the real-time of the algorithm, the tracking method based on Kalman filter is used in this paper. The algorithm firstly use Kalman filter to forecast the approximate position of moving object, and then the object are matched in the forecast area. In order to adapt to multi-objects, the method based on labeling connected domain is used to obtain the features of object and build the feature chain table. Then, a matching matrix is adopted to divide the tracking process into five situations: new object emergence, object disappearance, object sheltering, object separating and object matching. These situations are respectively analyzed and researched, and the flow charts about the situations are showed. Finally, the algorithm of matching and searching is discussed in detail, and the real-time of the algorithm is improved by using cross algorithm to match and search object. The experimental results show that proposed method can better adapt to multi-object detection, and obtain a more perfect objects tracking result.