A Study On Object Tracking In Image Sequences

Tracking visual objects in image sequences is an important topic in the field ofcomputer vision. This problem has received wide attention and it has a wide range ofapplication in different fields. In this dissertation, we discuss the problem of objecttracking in image sequences. The innovative points of the dissertation can be listed asfollows.In the traditional mean shift algorithm, only the color histogram is used fordescribing the feature of an object. The dissimilarity among the reference targets andthe target candidates is expressed by the metric derived from the Bhattacharyyacoefficients. The traditional mean shift procedure is used to find the real location ofthe object through looking for the regional minimum of the distance functioniteratively. However, in the case if the object moves slowly from frame to frame in theconsecutive image sequence or if the color histogram of the target is similar to thecolor histogram of the neighborhood, the algorithm does not work well. To overcomethe difficulty, a concept on the correlation of kernel density estimation of trackingregion and a new generalized distance is proposed. Experiments manifest thataccuracy and robustness of the tracking algorithm can be improved.We suggest an EM-like algorithm for object tracking based on the Gaussianmixture model (GMM). The algorithm uses the weighted summation of several Gaussfunctions to approach the probability function of position's distribution in twodimensional space. In the meanwhile, the GMM is related with the similarity measureof targets on the basis of their color features. Then, by converting the object trackingalgorithm into the EM estimation of object's parameters from frame to frame, the algorithm estimates the location of the object and updates the variance matrix,simultaneously, in each iterative step. Compared with the traditional Mean shiftalgorithm, the new algorithm presented in this paper can track the object more rapidlyand accurately, and update the object window adaptively.The traditional mean shift method does not work well when the target gets anocclusion. The tracking algorithm on the basis of particle filtering has the ability tooverpass the occlusion. Unfortunately, its performance relies heavily on the number ofthe used particles. This involves a large number of computations and therefore isdifficult to be implemented in real time. To settle the problem, this article brings abouta hybrid algorithm by combining the mean shift and the particle filter trackingtechnique on the basis of the color histogram distribution. By adopting the strategythat the number of particles is adaptively determined, it amalgamates the virtues of thetwo techniques. As a result, the computational cost can be reduced and the trackingperformance can be ensured simultaneously. The experimental results show that theproposed method is effective and robust.This article proposes the joint multi-target probability density particle filteringbased on the color distribution to address the problem of tracking multiple movingtargets with difficult conditions such as target crossing, target occlusion andbackground confusion. Here we use the kinematic prior as the importance probabilityfunction. Through comparing the Euclidian distance of the partitions that denotingdifferent targets, we can switch the procedure from the coupled partition particlefiltering to the independent partition particle filtering and inversely. The simulationand experimental results show that the proposed algorithm can track multiple objectseffectively.