Sports Visual Data Analysis with Deep Vision

The sports industry is surrounded by opportunities in all facets, including sports marketing, sports media, education institutions, sponsorship, and so on. They are adopting the newest technology such as computer vision to improve coaching task as well as benefit the sport broadcasting industry. In this paper, we examine action spotting in sports, particularly soccer. We found the SoccerNet challenge, which is a soccer video understanding challenge. SoccerNet provides a dataset for action spotting in soccer of 550 videos of famous leagues in recent years along with the event annotations in 17 classes. Action spotting is a concept that SoccerNet defined as finding the anchor time (or spot) that identifies an event. Inspired by previous successful methods under the action spotting task NetVLAD++ and AudioVid, we propose a multimodal architecture that learns temporal features of both visual and audio representation of video data. Our method achieves an mAP of 56.58%, which rises 3.18% higher than NetVLAD++ and 16.68% higher than AudioVid. Furthermore, we integrate that research into a web-based platform in order to contribute to the annotation industry. This platform consists of a client application written in ReactJS and a backend server. Both of them are deployed on the Google Cloud Platform so that users can experience our platform on any device without complex installation.