Research On Moving Target Tracking Algorithms In UAV Scene

Visual target tracking is an important task in computer vision and has been widely used in areas such as robotic service,human motion analyses and autonomous driving.The combination of target tracking and UAV technology enjoys a wide popularity with the development of high-performance computers,and great application prospects of UAV technologyin the military and civilian fields.Inspite of the remarkable improvement in target tracking in recent years,it is still a great challenge to establish a tracking model which can handle all complex situations such as complex background,illumination change and occlusion.This thesis focuses on the target tracking problem under the UAV scene,which is improving the real-time performance of the algorithm while keeping the algorithm robust and accurate.Aiming at the problem that the traditional compressive tracking algorithm is sensitive to illumination change and has poor tracking performance when the target is occluded,this thesis has proposed a predictive adaptive target compressive tracking algorithm.Firstly,the location prediction mechanism has been introduced to predict the target position in our algorithm,and the algorithm performs phase consistency transformation on the search area around the predicted position.Then,the classifier is updated adaptively according to the similarity of two target tracking windows.Finally,the updated classifier is used to determine the target position in the next frame.In this thesis,the proposed algorithm is fully validated by using traditional common public datasets OTB and UAV123.The results of the experiment are that the proposed method can better adapt to the illumination change and the occlusion of the target in the UAV scene,and can effectively improve the accuracy of the tracking results of the algorithm.Aiming at the problem that the tracking results of the correlation filter is easy to fall into the local optimal value and tracking failure under the background clutter,this thesis adds the context information into the framework of correlation filter tracking algorithm,which reduces the impact of complex background on target tracking and maintains high computational efficiency.The model based on spatial reliability canadapt to the deformation by establishing the spatial confidence map as the random field constraint of the corrrelation filter,so as to realize the adaptive to the irregular shape of the tracking object and effectively alleviate the boundary effect and reduce the complexity of correlation filtering tracking algorithm.The tracking performance is obviously improved,matching the requirement of real-time tracking of UAV.The experimental results indicate the superiority of the proposed algorithm on tracking.