Research On Object Tracking Algorithm In Presence Of Occlusions

Object tracking is an important branch of computer vision,which combines advanced technologies and research achievements in image processing,pattern recognition,artificial intelligence,automatic control,computer application and other relative fields.It include segment target,pick-up character and recognize objects.In practical application,the operating time of tracking algorithm must be also considered.Object tracking in a complex environment is very difficult.Only depending on one or a very few identify means is difficult to recognize objects accurately,so it need to make full use of multiple objects property and several means to track.This thesis mainly aims to analyze and solve occlusion problem in object tracking.It detailedly discusses the occlusion handling methods in several algorithms such as mean shift,kalman filtering,particle filtering,interacting multiple model etc,and makes effective improvement.The following is done in this thesis:Firstly,an improved algorithm is proposed by combining the advantages of the mean shift particle filter in this paper.Generally,the improved mean-shift algorithm is used to track objects;then the factor is given to judge the degree of occlusions by the searching formula.When the serious occlusion exists,the weighted particles are selected by the mean-shift to establish the update template.The algorithm can track the objects continually and steadily after occlusion.Experimental results show that the real-time algorithm tracks objects satisfactorily.It solves partial occlusion and full occlusion.So the degeneracy problem is efficiently overcome and the computational cost is decreased.Secondly,an improved algorithm based on Kalman filter and particle filter is proposed,in which Kalman filter is used to match the linear part of the system and particle filter is used to match the non-linear part of the system,the degree of occlusion is determined according to the match extent when the serious occlusion exists,the iterative multistage particle filter is exploited for re-sampling,then combined with Kalman filtering to update the model probability which can track the objects continually and steadily.Experimental results show that the proposed algorithm meets the real-time requirement,improves the speed of the model filter and the estimated accuracy of the object state,and reduces the computing time effectively.It also solves the occlusion problem in the process of tracking.