基于相机运动轨迹的鲁棒无人机航拍稳像算法OA北大核心CSTPCD

Aiming at the problem of high altitude turbulence environment to the time-delay stable image acquisition of UAV,an anti-shaking algorithm for aerial video was proposed for hovering shooting and moving shooting.Firstly from the time-delay photography video captured the UAV camera,some video frames were extracted globally to compare their histogram distributions.This comparison could identify whether the video contained active camera motion or not,and help categorize the video accordingly.For videos with active camera motion,FAST corner detection and optical flow methods were used to extract and match feature points.The RANSAC algorithm could remove all mismatched feature points,and estimate the camera's motion trajectory.The resulting motion estimation parameters were then smoothed using Gaussian filtering,producing a stable camera motion trajectory.For videos without active camera motion,the first frame was divided into grids and feature points were extracted based on Harris matrix.Optical flow tracking was carried out on these feature points in subsequent frames.Reverse optical flow and Harris matrix calculation were used to extract and match feature points,to increase the constraint of feature points.Finally,the retained feature points were used to estimate the stable transformation from subsequent frames to the first frame.Experimental results showed that the video classification module could correctly distinguish between the two types of videos.The algorithm was used to classify the video scene and stabilize the picture.Compared to other methods,this algorithm could improve the average peak signal-to-noise ratio of stabilized video images the most.For videos without active camera motion,the image could be absolutely stable,and the average peak signal-to-noise ratio of the image was increased by more than 39%,while the other two methods only by 10%to 12%.