Research And Application Of Video Object Segmentation And Foreground-background Harmonization Algorithm

With the advent of the era of short video,the information carried by the image can no longer meet the needs of people for social communication.It has become more and more common for people to create and edit short video.Short video editing mostly focuses on extracting specific objects and elements in the video,fusing with other scenes for secondary creation.This kind of task relies on professional editing tools for operation,so it will be accompanied by difficulties in software use and slow manual editing time.Therefore,the use of artificial intelligence method to assist can reduce the professional threshold and cumbersome operation,which has a broad application scenarios.In view of the demand of extracting specific objects and characters from video,this paper focuses on video object segmentation,aiming at solving the problem of automatic segmentation of user specified foreground elements through simple interaction.For the second creation scene,simply paste the segmented foreground object to the new background will cause contrast distortion and sense of violation.In this paper,the foreground-background harmonization algorithm is deeply studied,and the natural synthesis result is obtained through network processing.The main contributions are as follows:Firstly,a new semi-supervised video object segmentation network is proposed,and the information of the previous frame is incorporated into the network input to supplement the timing information.The similarity of two adjacent frames can significantly reduce the interference caused by occlusion and deformation.After extracting the features of each frame,the foreground and background pixels are separated and matched to improve the segmentation accuracy.By introducing the branch of difficult sample mining,the network can effectively improve the segmentation results of edge points and difficult points.Finally,a lightweight optical flow module is used to smooth the final result.Secondly,the foreground-background harmonization algorithm is proposed.Through the use of mask,this paper designs foreground background separation learning module,so that each feature can be separated and extracted for learning.At the same time,the loss function is improved so that the loss value will not change with the area of the foreground,which improves the robustness of learning.Thirdly,the platform of video segmentation and replacement is designed and implemented.Users can extract video elements and replace harmonious scenes through simple interaction.