Research On Object Tracking Method Based On Kernelizedcorrelation Filter

Computer vision is not only the key to realize the computer intelligence,but also the foundation of the development and progress of human society.As animportant branch of computer vision,intelligent video surveillance system has been developed rapidly.It has been widely used in human life.Moving object detecting and tracking technology is the most critical technology in intelligent video surveillance system,which has very important research value and significance.In recent years,the object tracking technologyresearch has achieved great development.However,the existing object tracking methods are still inefficient in some complex scenes.Therefore,the research of object tracking technology still need to continue.Among the various object tracking methods,Kernelized Correlation Filter tracking method not only has the advantage of fast tracking,but also has better robustness in solving lighting changes,slight occlusion and complex background problems.This article focuses on the research of the KCF object tracking method,and proposes an improved KCF object tracking method and LK-KCF object tracking method.Compare with the original KCF object tracking methods,the proposed methods respectively improve tracking accuracy underthe shape change and fast-moving object tracking situation.The main work and innovation of this article are as follows:(1)This article studies the basic ideas and principles of three typical target tracking methods firstly,including the optical flow method,particle filter and Online-boosting.Then thearticle realizes these methods with programming,and uses the experimental results to analyze application scenarios and the advantages and disadvantages of these methods.(2)Arming atKCF object tracking method's shortcoming of weak adaptability to shape change of the object,this article proposes an improved KCF object tracking method.The method includes improvements in two aspects: Firstly,the HOG-III and LBP featureof dimension reduction are fused to get the new feature descriptor,which has better description ability.Secondly,an improved appearance model updating strategy is proposed.This strategy needs to set up a parameter set for the appearance model of the tracker,and uses the average value of the parameters in parameter set to update the appearance model parameters.While the idea of random substitution is introduced into the updating process of the parameter set.Finally,the comparative experiment demonstrates the effectiveness of the improved KCF object tracking method for processing object tracking in shape change scenarios.(3)On the problem of losing object in the scene of fast moving object,this article presents a new tracking method for fast moving object,which named LK-KCF object tracking method.Firstly,this method uses the optical flow to rough locate the position of object.Then the method regards the rough locate position as reference candidate region,and uses the improved KCF object tracking method to correct the rough locate position,which can achieve the accurate position of the object.Finally,the simulation results demonstrates the effectiveness of LK-KCF object tracking method for tracking fast moving objects.