Study On Moving Object Detection And Tracking In Sequence Images

With the development of computer technology and improvement of computing power, acomplicated research topic, computer vision, attracts more and more attention, which aims tosimulate human visual capabilities. As a very important research interest of computer vision,the detection and tracking of moving objects in sequence images is to recognize the objects’information such as position, shape, velocity, etc. based on their temporal and spatialcorrelation, which has great significance to further vision-based applications. The detectionand tracking technology is facing many challenges such as object defomation, changefulcircumstance, unstable imaging devices, etc., which makes it still a challenging open researchissue to devise a robust, accurate and rapid detection and tracking algorithm. The dissertationexplored the concerned technologies by investigating the existing work such as backgroundmodeling, locating based on clustering, multi-feature fusion, contour evolution, filteringestimation framework, etc. and proposed innovative solutions to some related problems. Itsmain work and conclutions are as follows:Firstly, in order to improve the updating speed of Gaussian models for background, theconcept and computation method of the scene moving complexity were devised, according towhich a combinational Gaussian model for background modeling was proposed. In thismethod, according to the spatio-temporal sampling model of pixels, the scene movingcomplexity was analyzed and the entropy image of the scene was calculated. And this imagewas segmented into the stable region and the dynamic region by means of the maximumentropy threshold. In the two different regions, two different Gaussian models andcorresponding updating algorithms were respectively adopted. Since the modeling method isbased on reasonable analysis and classification for the surveillance scene, the proposed modelcan avoid the waste of Gaussians and be provided with higher updating and detecting speedcomparing to fixed number of Gaussians.Secondly, a object locating algorithm was proposed based on clustering analysis.Because the foreground resulted from background subtract often has many undesirable features such as abundance of outliers, spoiled connectivity, etc., the furter detection byreagion growing almost can’t locate the outline of the object correctlly. In order to solve theseproblems, a locating algorithm of moving objects was proposed based on neighboringanalysis. First, the foreground resulted from background subtract was downsampled andfiltered. And the locating of moving objects was converted to pixels clustering. Second, theneighboring feature matrix and the criterion function were proposed based on analysis of thedistance and context of the pixels. Finally, according to the minimization of the criterionfunction the clustering algorithm was devised. And the pixels were clustered into a certainnumber of clusters corresponding to objects. Thus, the foreground objects were locatedcorrectly.Then, the two tracking algorithms were presented based on in the framework of UKF aobject tracking algorithm was presented based on the region statistical characteristics.(1) Inthe framework of UKF a improved tracking algorithm based on the fusion of color and edgefeatures was proposed. The multi-feature fusion appearance model describes the object morecomprehensively than the single feature one, which has enhanced the accuracy and adaptivityof the tracking algorithm. At the same time, because of the effective “predict-update”mechanism in UKF, the iterations of mean-shift has greatly decreased and the searchefficiency for the tracked object is improved.(2) In the framework of Particle filtering, aobject tracking algorithm were presented based on region statistical characteristics of colorand optical-flow. In order to recognize various information of the object such as coordinates,speed, size and rotation, and to overcome the poor discrimination of color features, a trackingalgorithm in a hierarchical Particle filtering framework was proposed for the first time. Itadopted color and optical-flow features. The tests indicated it could adaptively adjust thetracking window’s position, size and rotation, estimat the target’s state correctly and track theobject accurately because of adopting hierarchical filtering stracture and two-feature fusionalgorithm. And it predicted the object’s position correctly under occlusion and catched itshortly when the occlusion disappeared, which showed it had a robust performance.Finally, in the UKF framework, a object tracking algorithm was proposed based on edge feature. During executing the algorithm of classical geometric active contour segmentation, toobtain accurate segmentation results always involves lengthy iterative process, which doesn’teven work. To improve the segmentation efficiency and accuracy, a novel detection andtracking algorithm was presented. First, the gradient image was calculated based on the vectorimage and an adaptive edge indicator was proposed. Second, the revised evolution modelusing variational level set method was put forward. And then the detection and tracking of theobject’s edge is presented in the framework of UKF. The experiments demonstrate not onlythat it has significantly increased the convergence rate and flexibility of the active contourevolution but also that it is robust to some interference such as shadow, occlusion,deformation of object and background interference.In summary, the dissertation has been presented in a comprehensive way to discuss theserelevant issues. And extensive experiments shows the proposed algorithms have been greatlyimproved in terms of roubustness and accuracy.