| Infrared imaging from mobile platform with high concealment,all-weather all-day work and other characteristics,is widely used in reconnaissance,surveillance and other military fields.Infrared moving target detection,as the basic work of infrared target recognition,tracking,behavior analysis,guidance,has been widespread concern.This paper aims at the detection of infrared moving objects with different background and different target characteristics under the condition of "moving platform,moving target and moving background".The main research contents include the following four aspects:First,preprocessing work on moving target detection is discussed.Aiming at the problem that the moving target detection is susceptible to noise,the effects of several image denoising algorithms are compared.In order to solve the problem of image registration,the influence of several different feature operators on image registration is discussed.The quantitative evaluation of moving target detection is put forward.Second,based on the traditional background modeling method,LBP background modeling and Low-Rank representation background modeling is used for moving target detection under motion imaging platform.This method is suitable for non-real-time detection,can be achieved accurate detaction results,but the computational complexity is higher.Third,the moving target detection with frame difference is improved based on infrared image characteristics.By analyzing the nonuniformity luminance characteristics of infrared sequence images,we use linear regression to fit the relationship between infrared sequence images.And,the gradient statistics histogram is used to correct the result of moving target detection with frame difference,improved the detection accuracy.Last,for single-frame moving infrared small-target detection,we propose an effective small-target detection approach based on multiscale gray difference and weighted image entropy.The experimental results show that the proposed method can effectively enhance the target and suppress the background. |
