Beskrivning
Machine learning as a field has expanded in an explosive manner, withmorecompaniesinterestedinusingthetechnology. Oneofthesecompa-
nies, Spiideo, uses Machine learning to automatically stream and record
sports, highlightingkeyevents-allautomaticallywithoutacameraman.
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 par-
ticular, 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.
| Period | 2021 juni 15 |
|---|---|
| Examinerad/handledd person | Elias Åkeborg |
| Examination/handledning vid | |
| Omfattning | Internationell |