Video Target Tracking Algorithm And Simulation Based On Particle Filter

Video target tracking plays an important role in today's daily life,and has a wide range of applications in civilian and military fields,such as autonomous driving,intelligent monitoring,and guided weapons.In recent years,with the rapid development of social technology,more and more Many scientists have devoted a lot of attention to target tracking,making it a hot research issue quickly.In this paper,particle filtering with superior performance is selected as the theoretical framework for target tracking,which can effectively deal with nonlinear or nonGaussian target tracking problems.However,in practical applications,the algorithm still faces constraints such as illumination,angle,size,and noise.Therefore,improving the robustness of the target tracking algorithm is still a challenging task.Aiming at the problem that the tracking target in the particle filter algorithm is disturbed by similar environments,for the problem of poor robustness of the single feature target tracking algorithm in video target tracking,a multi-feature fusion target is proposed according to the difference in the ability of the feature to distinguish the target Tracking algorithm.The algorithm can dynamically adjust the weight of each feature in the algorithm according to the impact of environmental changes on the effectiveness of the features and the different contributions of the different features to the target,and fuse the target state in the posterior probability distribution of different features to make it Target deformation,background light and dark,and partial occlusion of the target have a good response,thereby improving the accuracy and robustness of the tracking algorithm.For the particle degradation and lack problem in the iterative propagation process of particle filter,this paper uses kernel function to improve the traditional particle filter,so that after the target state is estimated,the algorithm calculates the distance between the particle and the center of the current target state,and dynamically The particle weights are corrected to improve the accuracy of the concentrated information of the particles during the resampling stage,to effectively suppress the particle degradation and lack of particle filtering,and to improve the stability and accuracy of target tracking.The multi-feature adaptive fusion strategy proposed in this paper and the improved tracking algorithm of kernel function modified particle set are compared in different tracking environments.The results show that compared with the traditional particle filtering and existing fusion methods,the improved particle filtering algorithm The effect has been significantly improved.