Real-Time Image Stitching For Multiple Camera Panoramic Video Shoot: A Case Study in Football Matches

In this study, a real-time image stitching method is proposed for cost-effective high resolution wide angle video shooting. In the first stage, the images taken from more than one camera with fixed position are stitched with a classical algorithm. After the parameters calculated in the first stage are stored, the image pixels are mapped using the stored parameters and ArUco markers. The mapping process is used for real-time panoramic video shooting after it has been calculated for that particular camera setup once. The images taken from the multi camera are combined by remapping with a GPU based approach. Therefore, registered mapping can be used in different environments without changing the position and lenses of the cameras. As a case study, real-time panoramic video is shot with two cost-effective cameras in football matches. Deep-learning based autonomous pilot video shooting is then performed on the high resolution panoramic video obtained. In experiments, 36 FPS speed has been reached by using a standard desktop computer and it has been seen that image quality measurements are at reasonable levels.