The Improvemengt Of Target Tracking Algorithm Based On Kcf In Complex Scenarios

With the rapid development of computer vision,target tracking is widely used in military reconnaissance,mapping,visual navigation and other aspects.Target tracking algorithm needs to select tracking target and extract target template before tracking.After the tracking starts,the position of the target in the latest image is predicted according to the target template.Many problems will be encountered in complex scenes,such as target scale change,target occlusion,target fast movement and target deformation and similar target interference,which affect the tracking effect.In this thesis,the kernel correlation filter(KCF)tracking algorithm is improved,so that it has better tracking effect in the complex background of target scale change,target occlusion,target deformation and light change.The works are as follows:(1)The KCF tracking algorithm can not be effectively applied when scale of the target changes or the target is occluded for a long time.In order to solve these problems,this thesis proposes an improved KCF tracking algorithm based on anti-occlusion and scale transformation.Firstly,the tracking target scale transformation is used to estimate the scale of target fastly.Secondly,when the target is occluded,the update of the classifier model is stopped,then the weighted window filter is used to predict the target position for the target tracking area.Finally,the algorithm is transplanted to DJI Guidance Vision Platform,and the experimental results show it can effectively solve the problem of target scale transformation and target occlusion.(2)In order to improve the tracking effect of KCF tracking algorithm under the condition of target deformation and illumination change,we propose an improved KCF tracking algorithm based on background target fusion.Firstly,it uses the combination of moving region segmentation and color feature histogram to extract the background target which is similar to the tracking target in the dynamic background.Secondly,it uses the STC context model to predict the position of the target,and fuses the tracking results with the original KCF tracking results to get the latest position of the target.Finally,background goals that do not meet the requirements will be discarded,after each update of the target position,the weight of each background target is updated and analyzed.Theexperimental results show that the improved algorithm has better tracking effect in complex environment such as illumination change and target deformation.