Research On The Detection And Tracking Of Moving Target Based On Statistical Methods

With the development and popularization of computer science, electronic technology,automatic control and artificial intelligence, the intelligent visual surveillance plays veryimportant roles in military, industry, human-computer interaction, intelligent transformand science researching, etc, and it is of a wide development prospect as well. So peoplepay more and more attention to the researches of sequential images motion detection andvisual tracking, which is a key technology of the intelligent visual surveillance and is anactive research topic in the image processing and computer vision. At present, movingtarget detection and tracking is not well-considered, many problems and difficult points intheory research and in applications are still unsolved. However, many issues such as weakdistinguishing image features, background clutter, occlusion, in addition, the targetmovement often behaves very complicated in real environment (e.g. target size, shapechanging, move speed and path, illumination changing, the similarity of target color andbackground color, and background stability, etc), can affect the effective observation of thetracked targets in images and make robust tracking algorithms designing a very difficultproblem. Therefore, the researching on moving target detection and tracking undercomplicated background has both important theory significance and application value.Aiming at resolving the difficult problems of the robust visual tracking, we studied severalkey technologies under complicated environment from movement target detection andsegmentation, target area feature acquiring, target describing, and robust tracking. Themain contents and contributions of this dissertation are summarized as follows:(1) The moving target detection and segmentation have been studied in stationaryscene. An effective adaptive background updating method based on Gaussian mixturemodel (GMM) was presented. The number of mixture components of GMM is estimatedaccording to the frequency of pixel value changes, the performance of GMM can beeffectively improved with the modified background learning and update, new distributiongeneration rule and morphological reconstruction based on spatial and temporal pixelsinformation. The detection of illumination great change and shadow removal were alsoproposed.(2) To enhance the performances of object tracking in stationary scene, a trackingmethod based on adaptive color segmentation and object part model was presented. In thiswork, the foreground blobs are obtained by background subtract. Object parts in the part model are generated online by the color segmentation based on mean shift andregion-growth. The constraints between parts and region features are taken into accountand used to perform objects tracking. The algorithm can solve the partial occlusion andobject deformation problem well.(3) To improve theoretic limitation of the traditional Mean shift, a novel targettracking algorithm was presented. Firstly, a new color space is partitioned into subspacesby considering the weighted number of pixels with feature vectors cluster, and describingthe pixel coordinates with Gaussian distribution. Then the target model and the candidateare constructed through an improved spatial histogram, which has ability to surmount thesimilarity between target and background. Finally, the affine transform is establishedcombining comer and edge detector to update tracking window. The algorithm is able tohandle the fast target tracking and occlusion by combining kalman filter and Mean shift.Besides, the algorithm has better effect and robust through eliminating the tracking errorusing the drift correction of target template.(4) To resolve the problem of single visual cue in different enviroments and posechanging, a particle filter tracking algorithm based on cues integration mechanism andadaptive observation likelihood is proposed. The multiple cues are adopted to representtarget, the likelihood model is constructed on-line with the reliable cues. Besides, hiddenvariables indicating geometric transformation are also augmented in the staterepresentation, and affine transformation is used to handle the movement changing oftarget.In the dissertation, the target detection and tracking algorithms have been studiedextensively under complicated environments, robust and practical methods were proposedto solve the key technologies, such as target segmentation, target modeling, occlulsion,and robust target tracking.