Research On Moving Target Detection And Tracking Technology Under Airborne Platform

The detection and tracking technology of moving target airborne platform has been widely used in aviation reconnaissance,disaster relief exploration,traffic monitoring and other fields,which has important theoretical value and practical significance.The video sequence taken under the airborne platform is affected by the platform motion and incorporates dynamic information of the background.At the same time,it also increases the difficulty of moving target detection,making it difficult to ensure its accuracy and reliability.For the tracking of moving target,platform motion will cause a large change in the shape and scale of the captured moving target,and the algorithm must satisfy long-time stable tracking.In order to solve the above problems,firstly this paper completed the estimation and compensation of the background motion under the airborne platform.Then it detected the moving target,locating it accurately,and finally realized the stability of the moving target tracking.Motion estimation and background compensation.This paper proposes a screening strategy to reduce the number of SURF feature points.After extracting the feature points of the reference frame image,the sliding window is used to traverse the image to remove feature points that are too local and close together.Then,using the nearest neighbor criterion based on the FLANN search strategy to match the feature points of the inter-image.Finally,the LS method is used to process the optimal interior point set estimated by PROSAC.Through the above steps,the parameters in the camera motion model are fitted,and the background motion compensation of the current frame is completed.Moving target detection.Considering that background compensation cannot completely eliminate the background motion differences in inter-frame images,in order to improve the autonomous detection ability of the algorithm,an improved moving average target detection algorithm based on statistical information is proposed.By using the frame difference method,the foreground and background parts is roughly separated,and the statistical information of the foreground pixel points is introduced in the moving average algorithm to update the background modeling weighting factor adaptively.After merging with the detection result of the three-frame difference algorithm,the detection of the moving object in the video sequence is completed.Moving target tracking.After focusing on studying KCF algorithm,the scale transformation of KCF is introduced by referring to other scholars' researches.According to the multi-feature fusion method,the HSI feature is integrated at the feature level whose the color information of target can enhance the description of the target.At the decision-making level,the weighted sums will be calculated based on the results of KCF and color histograms tracking algorithms.At the end,the experimental comparison and analysis on the UAV123 data set was completed.