Research On Fast Tracking Algorithm Of Small Target Under UAV Aerial Photography

With the rapid development of computer performance,computers and other intelligent devices are playing an increasingly important role in daily life.People's positioning of computers is also changed from the previous fully manual operation to the computer can learn independently and operate autonomously to achieve the real artificial intelligence.In order to adapt to the requirements of each scene and different tasks and improve the performance of computer vision applied in different working states,the research on algorithms of different branches of computer vision is becoming hot increasingly.Aiming at the present stage application scenario for unmanned aerial vehicle(uav)equipments,in order to target tracking algorithm as the research subject,the research is mainly focused on the target form and characteristics of unmanned aerial vehicle(uav)scenario,which is based on the traditional feature extraction technologies and feature extraction of convolution neural network technology,and combined with relevant filter for target tracking model building structure.The main research content is divided into following parts.This thesis studies the basic task of target tracking algorithm,which is to extract the position of the target in the video.Based on the task,the specific steps of the target tracking algorithm are proposed and divided into some independent stages,including target positioning and calibration,target feature extraction,target location and marking,etc.In the specific stage specific research,the main part of the target tracking algorithm is feature extraction and target classification,and then to each stage of detailed analysis and mathematical proof.Through the investigation and simulation of different algorithms such as KCF,DCFNet,SiamFC,CF,the extrated characteristics of different algorithms and classifier types are divided according to specific categories.Based on the overall performance of the algorithm,different features,feature extraction methods,and classifier methods are classified in terms of performance and applicable direction.Then the advantages and disadvantages of different models and the specific scenarios that may be applicable are summarized.This thesis proposes the assumption that the traditional feature extraction method and the neural network feature extraction method have the unity in the mathematical calculation process,and obtains the relationship between the depth feature and the traditional feature.According to the mathematical calculation of different classifiers,the formula is derived,and the specific influence of different feature extraction methods and specific parameters in different classifiers on the time and space costs of the overall target tracking algorithm model is obtained by combining the relevant mathematical calculation costs of feature extraction.According to the different applicable scenarios of different characteristics,the uav scenarios are analyzed in detail.Analyze the characteristics of the target in this scenario.According to these characteristics,the specific feature extraction and classifier methods are selected and improved,and the target tracking model for specific targets in this scenario is established.It is proposed that the model can be optimized by combining the moving feature of the target with the static feature of the target in the image and introduced the target motion state prediction module.