| dc.description.abstract | Manual clipping is currently the gold standard for extracting highlight clips from soccer games. However, it is a costly, tedious, and time-consuming task that is impractical and unfeasible for, at least, lower-league games with limited resources. Today, the manual method is to use a preset time interval, trimming away undesired video frames. To address this issue, this thesis aims to automate the generation of highlight clips for booking events. In our pipeline, we will implement logo detection, scene boundary detection, and multimedia processing. We will also do a statistical analysis of current highlight clips, and perform a subjective evaluation. Full games are used as input, where detection modules will locate possible timestamps to produce an intruguing highlight clip. Through experimentation and results from state-of-the-art research, we will use neural network architectures and different datasets to suggest two models that can automatically detect appropriate timestamps for extracting booking events. These models are evaluated both qualitatively and quantitatively, demonstrating high accuracy in detecting logo and scene transitions and generating viewer-friendly highlight clips. When looking at state-of-the-art research and the results in the thesis, the conclusion is that automating the soccer video clipping process has significant potential. | en_US |
