Font Size: a A A

Research On Moving Object Tracking Based On Mean Shift

Posted on:2009-05-09Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z Q WenFull Text:PDF
GTID:1118360278454061Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
For the technology of object tracking based on video can be used in fields of civilian and military affairs etc, such as video surveillance, robot navigation, human-computer interface and image compression, so it is quite important to study the technology of object tracking. Thus, this dissertation focuses on two key problems, namely initialization of object tracking and improvement on object tracking performance under the complex environments. Some new theories and methods are presented and are applied in field of robot navigation based on video. These fruits will contribute to further theoretical study and extensive application for technology of object tracking. Several aspects around these key problems are studied in this dissertation, and main contribution and work are described as follows:(1) For mean shift algorithm with Gaussian kernel function, its expression and its review is given and a convergence theorem with its rigorous proof is provided. Moreover, iterative step size along the gradient direction in mean shift is proved to be less than the optimum size and mean shift algorithm converges at a linear rate. Lastly, a fast mean shift (FMS) algorithm is presented, and the experimental results show that FMS can reduce the iteration number.(2) An approach based on maximum a posteriori is presented for moving object detection in complex video scenes. Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field model. For lack of feature information and low accuracy of detected object in traditional method, based on temporal Gibbs potential energy model and neighboring information, dependencies of consecutive label fields, spatial dependencies within each label field and edge features are merged into this framework by kinds of probability models.(3) By analysis on performance of object tracking based on mean shift, an object tracking method based on fuzzy kernel histogram is presented to reduce the localization error of object tracking. Two strategies are given for the fuzzy membership function to build the fuzzy kernel histogram. They are respectively ratio strategy and difference strategy. The experimental results of these two strategies are given and the advantage and disadvantage of these two strategies are discussed. Furthermore, errors of Bhattacharyya coefficient and its influence on object tracking are studied in this paper. Based the analysis on errors of Bhattacharyya coefficient, the optimization problem of Bhattacharyya coefficient is transformed into a constrained optimization problem, so an improved object tracking method is presented. In addition, the convergence of the improved object tracking method is proved.(4) For improving the robustness of particle filter, an adaptive particle filter is presented by creating the expression model of moving information. Furthermore, while tracking precision is low, mean shift algorithm is selectively used to optimize particles for improving the quality of particle. Tracking results is obtained according to the quality of particle.
Keywords/Search Tags:object tracking, object detection, mean shift algorithm, fuzzy kernel histogram, maximum a posteriori, particle filter
PDF Full Text Request
Related items