Automating Feature-Extraction for Camera Calibration Through Machine Learning and Computer Vision

Sammanfattning: Machine learning as a field has expanded in an explosive manner, with more companies interested in using the technology. One of these companies, Spiideo, uses Machine learning to automatically stream and record sports, highlighting key events - all automatically without a cameraman. However, these cameras have to undergo a lengthy calibration process involving manual feature extraction. This work investigates the usage of machine learning and computer vision to automate this work. In particular, both U-Net and DeepLab v3+ networks were trained on sets of images and related data from previous feature extractions. From the ML detected features, ridge detection and sub-pixel optimization was used to remove outliers and for classification. The accuracy of the ML and computer vision combination was compared to the manual feature extraction, yielding similar results. The DeepLab v3+ network was found to very accurately extract the intended features, leading to high accuracy independent of camera position, camera angle or noise from the stadium. Keywords: Machine learning, computer vision, feature extraction, classification, camera calibration, DeepLab, U-Net, ridge detection, sub-pixel optimization.