Cost-Optimized Event Detection in Football Video
In this thesis we aim to investigate if it is possible to detect events in football video using deep learning methods. The performance of a few different models using video input of varying frame rates is evaluated to find the most promising approach. We also evaluate if a proprietary dataset consisting of estimated player positions and movement can be used to detect events as it would be cheaper
