Study On Particle Filter Based Visual Tracking Method

This dissertation is an exploration on particle filter based visual tracking method. The aim is to improve the tracking stability under complex background and propose a practical method to track deformable object. Particle filter realize recursive Bayesian filter via Monte Carlo simulation. The method is suitable for any non-linear system that could be represented with state model. It is more practical than conventional Kalman filter and its precision could approach optimal estimation. Particle filter is flexible and easy to be implemented. And it is also has parallel structure. This dissertation studies on particle filter and its implementation. The method is used to solve tracking problem and the tracking framework is formed accordingly. The object is denoted with grey feature,contour feature and wavelet feature. And the particle filter based correlation tracking ,contour tracking and wavelet feature tracking methods are discussed. The concrete content of the dissertation include: extraction of the grey feature,contour feature and the wavelet feature of the object; the description of the visual tracking framework based on particle filter; implementation of each module of the framework; the analysis of tracking precision of the method, etc. This thesis mainly consists of the following parts: (1) Particle filter theory used for visual tracking field is studied. The meaning of each module of the tracking framework is explained in detail. The advantage of particle filter based method is discussed. (2) Particle filter based correlation tracking method is proposed. Using translation motion as an example, the robustness of the method is shown. Then the particle filter method is used for tracking in the affine space, and the result testifies that the method is practical for tracking in high-dimension space. (3) Snakes model is investigated in detail and the shape space is imported to limit the deformable extension. The particle filter based contour tracking method is studied and implemented. (4) Gabor feature template and particle filter based tracking method is proposed. The method is robust under the situation of that the object is partially blocked by a momentary obstacle, or the illumination and contrast changes, or a big change of the scale and orientation of the object is happened. The result of the experiments show that the method can reliably track a target with affine transforms under bad situation mentioned above. (5) The tracking precision is analyzed quantificationally finally. Diffusion radius and particle number are considered as two main parameters. The particle diffusion radius is chosen as two and a half time of the speed of the object. Then the tracking precision could be within 1 pixel when the particle number is the half of the searching amount of the conventional exhaustion method. The innovations of this thesis include the following parts: (1) Particle filter theory is used for visual tracking. The meaning of each module of the tracking framework is implemented and explained in detail. (2) Particle filter based correlation tracking,contour tracking and wavelet feature tracking are proposed and the experiments are carried out. The results show that the tracking precision could be with 1 pixel taking correlation tracking as an example. The computation consumption is smaller than the searching amount of the conventional exhaustion method. The tracking robustness is improved drastically. (3) The tracking precision is analyzed quantificationall. Diffusion radius and particle number are considered as two main parameters. The method for choosing the two parameters is proposed.