Research On Tracking Algorithm Of Moving Objects In Video Based On KCF

With the acceleration of the social intelligence process,the target tracking technology has been integrated into all aspects of people's lives.The purpose of target tracking is to continuously,stably and accurately track the target object according to the detection or manual selection of the target.The current target tracking algorithm has higher accuracy and faster real-time performance.It has moved from the traditional generation algorithm to the discriminant tracking algorithm.The related filter tracking algorithm proposed in recent years is a discriminant tracking algorithm.Significant effects in tracking speed and tracking accuracy.Based on the kernel correlation filter tracking algorithm,thesis studies the scale change that affects the tracking effect and the long-term tracking problem in the occlusion environment.The research in thesis is mainly from the following aspects:(1)The kernel correlation filter tracking algorithm is studied and analyzed.Firstly,the cyclic matrix sampling and different kernel functions are introduced in detail.Then the tracking process of the kernel correlation filter is obtained,which mainly includes training,detection and updating.And solve the multi-channel data processing problem,and finally give the overall flow of the kernel correlation filter tracking algorithm.(2)In view of the poor tracking effect and scale change problem in complex environment.Shape features and texture features are combined to improve the robustness of the filtering model.Using the scaled pyramid target scale search strategy,the position coordinates of the target are first determined,and then the scaled filter of different scale samples is used to determine the optimal scale of the target.(3)For the target tracking problem in occlusion environment.A combined confidence measurement method including occlusion information is proposed The confidence result is used to judge the tracking result.If the result of the confidence in the kernel correlation filtering algorithm indicates that the target is occluded,the block mean shift algorithm is introduced to track the target.Use local information to get the final location of the target.The detection model can correct the result,thereby reducing the risk of model drift,and effectively suppressing the occurrence of tracking failure,and achieving stable tracking of the target under occlusion interference.(4)The performance of the proposed two algorithms is tested based on the OTB-2013(Object Tracker Benchmark-2013)test set.The target tracking algorithm proposed in thesis is compared with the algorithm that currently performs well in the tracking domain.From the qualitative analysis and quantitative judgment,the two improved algorithms have good tracking performance when dealing with complex factors such as complex environment,scale change and occlusion.