UAV Special Behavior Detection And Recognition Based On Airborne Images

Video images usually contain rich information content.With the wide application of small multi-rotor UAVs in many fields,extracting and analyzing the target and content understanding based on the video information collected under flight conditions has also become a key task.It includes the detection and tracking of drone targets under the interference of complex background in low-altitude airborne images.As one of the widely used scenarios in the field of computer vision,the research objective of the topic is to face the airborne optical image,and study the unmanned under the background of "air-to-ground",the detection and tracking of aircraft moving targets,and the identification method of UAV special trajectory based on time series analysis is proposed.This paper combines UAV and vision technology to explore the characteristics of UAV motion video collected under the airborne camera.The research results can be applied to cluster UAV interaction,low,small,and slow UAV defense,etc.scene.First of all,the video collected by the small rotor drone will have an impact on the target detection and tracking algorithm due to the complex "air-to-ground" imaging background,changeable textures,lighting effects,and height changes.Generally,using traditional image features to train classifiers generally uses a sliding window to classify and discriminate possible candidate target regions in the entire image,which consumes a lot of time,and can easily lead to misrecognition problems when the complex background repeats texture interference.Therefore,this paper first uses global motion compensation to eliminate the background displacement caused by camera shaking,then uses the multi-frame difference method and combines the contour detection method to obtain the moving target area,and then combines the classifier to screen and recognize the UAV target,and use it as the UAV target follow-up the initial region of interest to track.In order to meet the real-time tracking effect of the drone scale changes on the target under the airborne image,the multi-scale dimensional space coring correlation filter algorithm is used,and the tracking algorithm is re-detected by the moving target when the tracking of the background texture is similar and the overlapping area is invalid.The initialization ensures the continuity of the tracking trajectory,so as to facilitate the detection,extraction and identification of the UAV trajectory type,and to understand the UAV behavior.Then,in the research on the detection and recognition of UAV special behaviors,four types of simple closed-loop trajectories used to describe the special behavior of UAVs were first defined,and the special trajectories were extracted using a hierarchical sliding window search algorithm,and the improved feature-optimized hidden Markov model is used to classify and recognize the tracking trajectory category of the UAV.At the same time,for the type matching problem of the non-equal time sequence signal of the UAV trajectory type recognition,the traditional dynamic time warping method is analyzed.The advantages and disadvantages of the improved Fourier spectrum feature normalization descriptor and the window condition limit of the linear search space are proposed,and the trajectory recognition accuracy rates of different methods are compared.The results show that the improved method is fast and accurate in recognition rate has been improved.Finally,this article combines the previous theory and research results,uses the improved UAV target detection algorithm to process the global motion compensation airborne image to obtain the UAV target detection result,and then uses the detection result as a multi-scale unmanned The initial value of the machine target tracking and the tracking trajectory are obtained,and the processing results of the trajectory recognition algorithm before and after the linear search space improvement are compared and analyzed,and the follow-up research directions are summarized and prospected.