Research On Tracking Method Of Moving Target In Complex Background

Moving object tracking is one of the most important research fields in machine vision,and it has been widely used in many fields,such as intelligent surveillance,intelligent traffic control,UAV vision navigation and so on.However,due to the complex and changeable background,the target tracking process is often influenced by the factors such as illumination change,background disturbance,target shape rotation,target out of sight,occlusion,etc.,how to track the target accurately and quickly is still a hot issue for scholars and researchers at home and abroad.The research of moving target tracking method in complex background has important theoretical significance and engineering application value.To solve the problem of moving target tracking in complex background,the thesis improves the tracking method to improve the success rate and tracking accuracy.The research contents are as follows:1.In the process of target tracking,the object is occluded,the illumination changes,the target moving too fast,the target is blurred on the image and the object rotation,all these will cause the appearance changes of the object,in addition,the fast moving speed and low resolution of the target will lead to the lack of the search area and cause the target frame drift.All these will make the response graph abnormal in the process of tracking detection,which will reduce the success rate and accuracy of the tracking algorithm.Therefore,in the thesis,based on efficient convolution operator,an improved anomaly suppression target tracking algorithm is proposed,which combines the occurrence of the anomaly suppression with the training process of the tracker model,and greatly reduces the occurrence of the response graph anomaly,and improves the tracking success rate and accuracy of the target tracking algorithm.2.If the tracked object is out of sight for a long time,it will cause the tracking model updating wrongly,which makes the tracking algorithm fail and can't continue tracking.To solve this problem,a long-term target tracking method based on HSV color histogram is developed.A classifier is trained by using the color histogram information of the target,and the tracking state of the target is analyzed by outliers.When the object is occluded or disappeared,the classifier searches the whole image to find the object which is similar to the object's color histogram and judge whether it is the tracked object by comparing the confidence level,if the result show that it is the tracked object,the tracking module is started to continue tracking.3.Simulation experiments are carried out on otb-100 and vot2018 lt datasets.The experimental data show that the success rate and accuracy of the algorithm are improved before and after the improvement,which proves the effectiveness of the algorithm in complex background.