Research On Object Tracking Techniques Based On Bayesian Statistics And Inference

Computer vision is entering many aspects of human routine.The development of vision in silicon has become a critical driving force to propel society progress.More advanced machine vision means a more stable,more accurate,more efficient procedure of automatic recognition and tracking of specific target by computer,and a suitable tracking algorithm is the key element of robust vision system.Based on the profound background above,this article make an intensive study on the Object tracking algorithm under the Recursive Bayesian estimation theoretical framework.The article investigated the difference of two strategies in tracking algorithm designing,depicted the tracking performance of Kalman filter algorithm and particle filter algorithm through computer simulation of target tracking by matching of feature in HSV color space.The main work of the article can be summarized as follows:In the beginning of the article,the basic theory of the research is depicted,includes Bayesian statistical inference models,color space model and the algorithm of calculating digital color histogram.Then the theoretical framework of Recursive Bayesian Method using statistical inference was discussed which provides a theoretical supporting point to applying in target tracking problems.From the perspectives of Maximum a posterior probability and Minimum Mean Squared Error,summarizes the algorithm of computing the state posterior mathematical expectation estimator of a linear system,research recursive algorithm model in video tracking,implement a computer simulation that tracking a billiard in a webcam video.From another point of view that computing an approximation of the state posterior probability density,article described the fundamental concepts of random simulation method and it's applying in Bayesian probability computing.Researched and summarized recursive estimator of posterior density about state—an importance sampling method and development in resampling.The last part of the article is an experiment on Bootstrap particle filter video target tracking algorithm.Firstly a detailed description of the target state model and measurement method is assumed.At the measurement of determination in target area color histogram similarity,implement the stable tracking the central position and scale of target area changes in the video signal.The ending of the article discussed real-time performance of particle filter video tracking algorithm which has a critical influence on tracking performance should be enhanced with the development of multi-core processors and parallel computing technology.Then framework of particle filter tracking algorithm will be a general adaptive choice for video tracking problem.