Real-time video stitching, which is used to obtain a large field video by some small field cameras, has great significance in real life. The existing video mosaic method based on SIFT features and RANSAC algorithm takes too much time in the processing of the first frame image, and the transformation matrix will have large errors when the number of the feature points matched correctly is small. In this paper, a real-time video stitching method based on ORB features and support vector machine (SVM) using binocular cameras is proposed. Firstly, the distortion of the cameras is corrected. Secondly, the ORB features in the overlapped regions of the first two frame images are extracted. Each pair of the feature points matched is filtered through the pre-trained SVM model. The matching calculation is terminated after 4 pairs of feature points are obtained and the transformation matrix can be calculated. Finally, the video stitching result can be obtained by image registration. The experiments show that the real-time seamless wide-field video can be obtained, and the first frame processing time of this method is much shorter than the other methods available, the frame frequency is 30fps.
|