Studies On The Target Tracking Algorithm Based On Particle Filters

Target tracking is becoming an active research topic in the areas of computer vision. The essence of target tracking is interactively searching in image sequences to validate the location of a certain object with some salient visual features (such as color, shape, texture, motion). With the rapid development of image processing and the improvement of computer, the technology of target tracking, has been widely used in video surveillance, video retrieval, human-computer interaction, traffic control, medical diagnosis, robot navigation, virtual reality and image guidance and so on.The algorithm of target tracking based on particle filter is more popular among many algorithms in recent years. Particle filter is one of the powerful tools for non-Gaussian/nonlinear state estimation problem, which is also a complete theoretical framework for target tracking. The algorithm is based on Bayesian statistical models. The tracking problem is converted into the non-Gaussian and nonlinear Bayesian filtering problem. Then target tracking can be implemented by solving the filtering problem.Spatiograms outperform the traditional color histogram, which contain color information and spatial layout of these colors for the target. In particle filters framework, the posterior distribution of the target is approximated by a set of weighted samples and a normal random drift model is utilized to describe the state model. The targets are represented by the spatiograms, which is defined by the spatial information of the color distribution estimated by kernel-based density. A similarity function of spatiograms is used as the probability model for observation. Finally, we propose a target tracking algorithm using particle filters based on spatiograms.The infrared target in sea clutter background is difficult to be tracked robustly with the target detection reduced, which is vulnerable to the impact of sea clutter. In this thesis, the target tracking algorithm using particle filters based on spatiograms is applied to infrared target tracking in sea clutter background. The algorithm which refers to spatial information using a more stringent definition of similarity than the traditional particle filters based on the gray histogram can realize to tack the infrared target in sea clutter robustly and solve the problem of occlusion effectively. Experimental results indicate that the proposed algorithm outperforms the traditional particle filters based on the gray histogram, and has better robustness under local occlusion and global occlusion of infrared target tracking in sea clutter background.