Abstract: This paper presents a moving object tracking algorithm based on genetic particle filter which is constructed with boosting algorithm and genetic algorithm.The object information and background information are used to construct feature classifiers,the output results of these classifiers are taken as the important observation information for the particle filter system and are used to calculate particle coefficients.These classifiers are updated during tracking so that the particle coefficients are updated adaptively.Genetic algorithm is introduced into the particle filters to improve the real-time ability of the algorithm.On the premise of guaranteeing the accuracy of the particle filters,the number of particles is considerably reduced and the processing time is decreased.The experiment result shows that the proposed algorithm can adaptively select features according to different background information,and can carry out stable and real-time tracking event if covering, deformation and background interferences exist in the environment.