Particle Filters In Target Tracking In Image Sequences

Automatic sequential image target tracking is of vital importance in both civil and military use. Demand of researching and developing tracking algorithms and systems with high reliability and high real time have been more and more these years. Because vision procedure is actually nonlinear/nongaussian, using the corresponding techniques become one kind of important research tend in image fields. Particle filter is one of theories as description above, and has distinguishing features. In this paper key issues of applying the theory to target tracking are studied.Particle filter is an inference theory for estimating the motion state from a noisy collection of observations, the core content in realizing target tracking with this theory is to construct a target tracking model and to form a corresponding automatic process. Two important components of this particle filter are state transition model and observation model, which are built according to the results of target detection. There are two kind of motion in dynamic sequential image: target motion by itself and ego-motion of image due to the camera. To detect the target using the difference method that is suitable usually when the scene is fixed in the case of dynamic sequential image, ego-motion compensation is implemented efficiently by features correspondence with Shi-Tomasi-Kanade features tracker and "pyramid structures" and then features reliability analysis. In addition, an improved frame difference approach that makes up for the shortage of the based one is proposed and by which not only the position but also the direction of moving target can be recognized. Given that the position of the moving target is the unknown motion state and the difference images by ego-motion compensation are the sequential observation datum, a particle filter based on motion detection is built, and automatic target tracking turns into reality with it at the last.The approach developed in this paper is experimented to the pedestrian in the environment indoor and show that it has an effective, reliable and sub real time tracking outcome when the displacement between the consecutive frames.