Detection And Recognition Moving Objects With A Moving Camera On UAV

In the field of computer vision,the detection and recognition of moving objects in the context of moving is a very important research content.As a new type of mobile platform,the drone has the characteristics of high flexibility and strong mobility,and has been more and more widely used in many fields.Because the background of the video collected by the UAV camera is moving,common moving target detection algorithms have large errors.For this difficult problem,combined with the background subtraction has the advantages of small computational complexity,can adapt to a slightly more complicated scenario environment,and has certain robustness to the interference,etc.This paper proposes a moving target detection method for UAV cameras.Recognition algorithm,the specific work is as follows:(1)Comprehensive analysis of the current research status of the moving target detection technology at home and abroad,designed a system overall plan for the UAV platform motion target detection and recognition algorithm,build an experimental platform,and calibration of the onboard UAV camera.(2)In order to solve the difficult problem of establishing the background model of airborne UAV camera movement,this paper improves the traditional single Gaussian background model and proposes a dual-mode single Gaussian background model with age entry.This model introduces variable update rate,which can Effectively reduce the influence of registration errors on the model and the pollution of foreground pixels to the background model.By comparing experiments,the superiority of the dual-mode single Gaussian background model compared with the traditional single Gaussian background model is verified.In addition,an adaptive motion compensation method is proposed for the large degree of freedom of drone flight.The method has a small amount of calculation and high accuracy,which improves the speed and accuracy of motion compensation.(3)There is a problem of misjudgment or omission in the foreground pixel segmentation map after the decision.This paper optimizes the detection results,proposes a shadow removal method based on gray similarity,and a hole removal method based on probabilistic morphological expansion,which makes it possible to detect The moving target is more complete and accurate.At the same time,prior to the removal of the shadow operation,a shadow existence determination algorithm based on the ratio of the energy value and the target shadow pixel number is used to further reduce the calculation amount.(4)Targeting the moving targets detected by drones needs to be quickly identified.The Mobile Nets-SSD neural network based on GPU is used for target recognition.The GPU acceleration model is used for training and running.At the same time,the model iscompressed using Mobile Nets.The Mobile Nets-SSD running on the GPU runs nearly seven times faster than the SSD.Experiments were conducted under six different environmental conditions.The experimental results show that the proposed method can effectively solve the problem of detection and recognition of moving targets in the moving background.The target segmentation quality is high,the operation speed is fast,the recognition effect is good,and the overall operating speed of the system can reach the highest 22 FPS.