Research On The Technology Of Detecting And Tracking Moving Target In Video Sequence

Moving target detection and tracking has become one of the key technologies in videoimage processing, and has been widely used in the field of human-computer interaction, videosurveillance, traffic safety and public safety management. On the basis of the researches on themoving target detection and tracking algorithms, this paper proposed some improvedalgorithms, and the effectiveness and the stability of the algorithms have been verified byexperiments. The main works of this paper are as follows:First of all, in the moving target detection, through analyzing the existing detectionmethods, and further study of the target detection based on codebook model. Codebook modelis a kind of detection method based on motion information. When the motion information oflargest is insufficient, there may be error detection or local detection and so on. Aiming at theshortcomings of the codebook model, this paper proposed an algorithm of the adaptivebackground based on codebook model by the joint space overall information of the target, toreduce the error detection in the process of the slow moving target and local moving targetonly. The method through analyzing the changes of mining target spatial information, to findthe background in the foreground, then combined with temporal statistical information toobtain the real background model. The experiments result show that, the algorithm can quicklyadapt to the changes in the background, improve the processing ability of codebook model,reduce the error detection significantly when the motion information insufficient.Secondly, in target tracking, this paper focuses on the target tracking algorithm of KalmanFilter and Mean Shift. In the view of the precision is not high based on multi-environmenttracking, and most of them use single way to implement the multiple feature fusion problem. Amultiple feature fusion based multiple criterion decision adaptive was proposed. Firstly,introducing the local background information to s strengthen the description of the target andthen using a variety of criteria adaptive to calculate feature weights in the process of multiplefeatures fusion. In addition, the framework of mean shift combining with Kalman Filter was considered to realize target tracking. An extensive number of comparative experimental resultsshow that the proposed algorithm has better stability and robustness than the single fusion ruleand improves the tracking accuracy in complex environment significantly.