Automatic analysis of sport events in video sequences

These last years have been an increasing need regarding the automatic sport analysis. Being soccer one of the most watched sports around the world, action spotting for soccer videos has become one of the main field studies in computer vision. In this project, we have done a deep research and analysis of the State of the Art regarding human recognition and action recognition in soccer videos. Apart from this more theoretical part, we have also executed and compared two of the most pioneering models in sports action spotting today: CALF[3] and NetVLAD++[4] models. In addition, we have been able to make a modification in one hyperparameter, the window size, to evaluate its effect on performance. Finally, we have concluded that we get the highest mAP performance with the NetVLAD++[4] model and that modifying the window size worsens the overall performance in both models, but the individual class performance can benefit from it as the performance is improved in some classes for different window sizes.