Research On Real-time Tracking Algorithm Of Video Face In Complex Background

Video human face is not as fixed as planes,cars and other machines,it is easy to change because of features,hair.Therefore,human face has characteristics of non-linear,non-Gaussian and multi-Modal,particle filter(PF)is an effective tool for such estimation problems.In the actual target tracking applications,face tracking in complex environment is increasing many problems due to the similarity between human face skin color and the background color,posture change,occlusion,and so on.Consequently,it is of great challenge to use particle filter to achieve real time,robust face tracking.The main work includes:(1)A genetic particle filtering algorithm based on Gauss-Hermite Filter(GHF)is proposed because of the blindness of the selection of importance density function and the depletion of the resampling.The new algorithm,on the one hand,uses a Gauss-Hermite Filter to produce the importance probability density function,which is incorporated into the latest observation data based on the transfer probability of the system state.On the other hand,this algorithm adopts the genetic resampling algorithm not only to improve the particle degradation but also to ensure the diversity of particles.At the same time,it requires very few particles to achieve accurate tracking.This improved algorithm improves the accuracy of state estimation and improves the robustness of the algorithm.(2)Traditional target tracking algorithm based on color feature is difficult to track targets accurately under the complex background of illumination change,human face rotation,scale changes and occlusion.In this paper,edge feature is added to the algorithm due to color-based method is easy to lose target.Meanwhile,an effective method for calculating the fusion coefficient is proposed.When tracking a target,our algorithm combines the color feature and edge feature adaptively,so that the target feature has good robustness and effectiveness.The maximum likelihood observation value is obtained by the ideal likelihood observation peak distribution,and the estimation of the closest realistic target state is obtained.Our algorithm can automatically and dynamically adjust the weight of different features,combining the information of color feature and edge feature adaptively.So it can enhance the observationeffect and the adaptive ability in the target tracking.In addition,the object model is updated between the current object model and the initial model to alleviate the model drifts.Experimental results show that the improved algorithm improves target tracking performance,which can robustly track human face with the cases of complex backgrounds such as similar skin color,illumination change and occlusion.This paper attempts to solve the problem of poor tracking precision due to the complex background,and to speed up the algorithm by integrating light compensation and integral histogram operation in the improved PF algorithm.