Abstract: A video recognition method and apparatus and storage medium are disclosed. In the method, n first eigenvectors corresponding to each of m first image frames of a first video is determined; the first eigenvector represents a spatial eigenvector of the corresponding first image frame; a second eigenvector is extracted from the first eigenvectors, and the second eigenvector is processed through a fully connected layer to obtain a third eigenvector; the second eigenvector represents a time sequence eigenvector corresponding to the m first image frames; a first behavior type between first and second object corresponding to the first video is determined based on the third eigenvector; each element in the third eigenvector represents probability of a behavior type; and when the first behavior type is a set behavior type, a video recognition result of the first video is determined based on the first behavior type and type of the second object.

Abstract: A device is provided for adjusting brightness of a plurality of images each including a plurality of pixels. The device may include a memory configured to store instructions. The device may also include a processor configured to execute the instructions to determine overall luminance values of the images. The processor may also be configured to determine, from the images, a reference image and a reference overall luminance value based on the overall luminance values. The processor may further be configured to determine adjustment factors for the images based on the reference overall luminance value of the reference image, determine weighting factors for the pixels in an image to be adjusted, and adjust luminance values of the pixels in the image to be adjusted based on an adjustment factor for the image and the weighting factors for the pixels.

Abstract: A dual fisheye lens calibration method includes: generating a first pair of fisheye images of two calibration chessboards located directly in front of two fisheye lenses and one or more random feature point calibration boards located in overlapping regions of the two fisheye lenses; adjusting positions of optical centers of the two fisheye lenses based on differences between projection coordinates of corner points on fisheye images and coordinates of images of the corner points on the first pair of fisheye images, to obtain optimal optical centers; generating a second pair of fisheye images of the calibration chessboards and random feature point calibration boards, based on the optimal optical points; generating a panoramic image based on the second pair of fisheye images; and adjusting distortion polynomial coefficients and extrinsic parameters of the two fisheye lenses based on a difference between panoramic-image coordinates of a matching feature point pair.

Abstract: A dual fisheye lens calibration method includes: generating a first pair of fisheye images of two calibration chessboards located directly in front of two fisheye lenses and one or more random feature point calibration boards located in overlapping regions of the two fisheye lenses; adjusting positions of optical centers of the two fisheye lenses based on differences between projection coordinates of corner points on fisheye images and coordinates of images of the corner points on the first pair of fisheye images, to obtain optimal optical centers; generating a second pair of fisheye images of the calibration chessboards and random feature point calibration boards, based on the optimal optical points; generating a panoramic image based on the second pair of fisheye images; and adjusting distortion polynomial coefficients and extrinsic parameters of the two fisheye lenses based on a difference between panoramic-image coordinates of a matching feature point pair.

Abstract: A device is provided for adjusting brightness of a plurality of images each including a plurality of pixels. The device may include a memory configured to store instructions. The device may also include a processor configured to execute the instructions to determine overall luminance values of the images. The processor may also be configured to determine, from the images, a reference image and a reference overall luminance value based on the overall luminance values. The processor may further be configured to determine adjustment factors for the images based on the reference overall luminance value of the reference image, determine weighting factors for the pixels in an image to be adjusted, and adjust luminance values of the pixels in the image to be adjusted based on an adjustment factor for the image and the weighting factors for the pixels.