Abstract: A video processing method, including: acquiring a plurality of raw videos, the plurality of raw videos being videos acquired by a plurality of dynamic image recorders arranged according to preset locations; determining an overlapping region between every two adjacent raw videos according to preset rules corresponding to the preset locations, the adjacent raw videos being the raw videos acquired by the dynamic image recorders arranged adjacent to each other; performing multi-stage optical flow calculation on the raw videos in each overlapping region to obtain a plurality of pieces of target optical flow information; and splicing the overlapping region of every two adjacent raw videos based on the target optical flow information to obtain target videos.

Abstract: A video processing method, including: acquiring a plurality of raw videos, the plurality of raw videos being videos acquired by a plurality of dynamic image recorders arranged according to preset locations; determining an overlapping region between every two adjacent raw videos according to preset rules corresponding to the preset locations, the adjacent raw videos being the raw videos acquired by the dynamic image recorders arranged adjacent to each other; performing multi-stage optical flow calculation on the raw videos in each overlapping region to obtain a plurality of pieces of target optical flow information; and splicing the overlapping region of every two adjacent raw videos based on the target optical flow information to obtain target videos.

Abstract: The present disclosure relates to a method and a system for joint optimization of an ISP and vision tasks, a medium and an electronic device, which belong to the field of image processing and can effectively avoid the over-fitting of joint optimization of the ISP and the vision tasks. The method for joint optimization of the ISP and the vision tasks includes the following steps: performing image signal processing on raw image dataset by an ISP to obtain processed image dataset; measuring probability gradient of the processed image dataset in prior distribution of traditional image dataset by a measurement module; and performing vision tasks on the processed image dataset by using a loss function with the probability gradient as a regularization term via a neural network.