A video inpainting method for unmanned vehicle based on fusion of time series optical flow information and spatial information. (30th October 2021)

• Record Type: Journal Article
• Authors: Zhao, Rui; Li, Hengyu; Liu, Jingyi; Pu, Huayan; Xie, Shaorong; Luo, Jun
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 5(2021)
• Publication Date: 2021-10-30
• Subjects: Optical flow -- deep learning -- generative adversarial networks -- video inpainting; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298814211053103
• Languages: English
• ISSNs: 1729-8806

Abstract

In this article, the problem of video inpainting combines multiview spatial information and interframe information between video sequences. A vision system is an important way for autonomous vehicles to obtain information about the external environment. Loss or distortion of visual images caused by camera damage or pollution seriously makes an impact on the vision system ability to correctly perceive and understand the external environment. In this article, we solve the problem of image restoration by combining the optical flow information between frames in the video with the spatial information from multiple perspectives. To solve the problems of noise in the single-frame images of video frames, we propose a complete two-stage video repair method. We combine the spatial information of images from different perspectives and the optical flow information of the video sequence to assist and constrain the repair of damaged images in the video. This method combines the interframe information of the front and rear image frames with the multiview image information in the video and performs video repair based on optical flow and a conditional generation adversarial network. This method regards video inpainting as a pixel propagation problem, uses the interframe information in the video for video inpainting, and introduces multiview information to assist the repair based on a conditional generative adversarial network. This method was trained and tested in Zurich using a data set recorded by a pair of cameras mounted on a mobile platform.