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Research on Methods of Target Tracking Based on Particle Filter Algorithm

Author: YangYong
Tutor: ChenAiBin
School: Central South University of Forestry Science and Technology
Course: Applied Computer Technology
Keywords: Target tracking target detection background modeling adaptive particle filter Bhattacharyya distance
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 163
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Abstract


Visual target tracking has a very important application in the field of video surveillance, image compression,3-D reconstruction, robot techno-logy and so on. The difficulty of target tracking lies in the sudden movements, or the sudden change of the external performance form of the targets or the background, non-rigid structure of the targets, the block among goals, the block between the objects and the background, and cameras movement. This paper mainly studies the moving object tracking in the complex background.The major work performs are followings:Firstly, particle sampling and re-sampling methods are studied; Simulation experiments are done according to the state estimates of nonlinear, non-Gaussian system by using particle filter; Comparative experiments are given about the standard particle filter and particle filter rules with the sampling method (RPF particle filter); The tracking errors of standard particle filter and RPF particle filter method are discussed according to the experimental results.Secondly, theoretical foundation of single Gaussian model and Gaussian mixture model, parameter estimation and the background update formulas are analyzed; The advantages and disadvantages of the effects of the Gaussian mixture background model and single Gaussian background model are given by simulation analysis. On this basis, in order to research the situation when there are new objects into or out of the current scene, the improved Gaussian model algorithm is proposed, which will bring risk decision into the sudden judgment of goals of the prospect. And the experiments verify the effectiveness of the proposed algorithm.The third, the traditional particle filter forecasting method is improved. It needs to calculate the two Bhattacharyya distances between the particles and target color model, and between the particles and the target texture model. The distances serve as the important basis for updating particle weights. The weighted sum of weights based on the color and texture is regarded as comprehensive weights, which can reflect characteristics of the background and targets, so as to effectively meet the changes of the background and objectives when tracking goals. The adaptive particle filter algorithm is proposed based on a multi-information fusion, which brings in a self-adaptive observation model in the framework of particle filter. In the algorithm, two state transition equations and three measurement equations are given, which can make particles to choose the appropriate measurement model and the state transition model according to the complexity of different backgrounds. At the same time, in order to further reduce the effect of the external factors such as the deformation of targets, sunshine etc, color histogram and texture histogram are adaptively updated in the process of tracking. Experimental results show that the algorithm can accurately track moving targets on a strong anti-interference and robustness.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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