Research On Vision Detection And Tracking Technology Of Ground Vehicle On UAV Platform

In recent years,UAV technology has made a long-term development in military and civil fields.In order to further expand the application scenario of UAV,the current UAV technology is trying to combine with other technologies to meet the practical needs of various industries for air operations.Among them,UAV vision detection and tracking of ground moving targets is still one of the topics worthy of in-depth study.Based on UAV platform,computer vision technology is used to realize vision tracking of ground vehicle target in image.At present,there are still many difficulties to realize the vision tracking of ground vehicles: on the one hand,during the process of vision tracking,it is necessary to deal with the variation of illumination,rotation,scale and partial occlusion of targets;on the other hand,after completing vision detection,it is necessary to distinguish whether the target is lost or not in the process of vision tracking.In this paper,the above problems are studied to solve the problem of detecting and tracking the ground vehicle on the UAV platform.The vision detection algorithms of ground vehicles are compared and studied.The vision detection algorithm based on deep learning is used to detect targets because it does not need to design complex feature extraction operators for specific targets and has high detection accuracy.in order to improve the accuracy of the vision detection algorithm,the sample data are enhanced considering the variation of illumination,rotation,scale and partial occlusion in the moving process of the ground vehicle target.In order to further accelerate the convergence speed of the neural network model,the current optimization algorithms are analyzed and compared,and the Adam optimization algorithm is selected to improve the different scale targets;in order to improve the recognition and detection effect of targets of different scales,target fusion and prediction are carried out on different level feature maps.The vision tracking algorithm of ground vehicle is studied and improved.At present,there are still some problems in the vision tracking task of the ground vehicle,such as tracking the continuous deformation of the target,partial occlusion,scale variation,target loss and so on.For the above problems,multi-feature fusion is introduced to further improve the descriptive ability of feature extraction operators to target features;response map analysis is used to judge whether the target is lost or not;for the adaptive updating of the model,according to the maximum response.The value and response spectrum fluctuation adaptively adjust the updating rate of the model to improve the anti-interference ability of the algorithm.Compared with the tracking method realized by detecting the target in each frame image,the proposed algorithm improves the average frame rate of vision tracking from 18.2 frames per second to 56.2 frames per second while ensuring high tracking accuracy.