论文标题
检测关键足球比赛以使用计算机视觉创建突出显示
Detecting key Soccer match events to create highlights using Computer Vision
论文作者
论文摘要
研究和数据科学界一直对自动系统的开发着迷，以检测视频中的关键事件。该领域的特殊关注是对体育视频分析的特殊关注，这可以帮助识别比赛期间的关键事件，并有助于为未来的游戏制定策略。对于本文，我们选择了足球（足球）作为一项运动，我们希望通过计算机视频模型为给定的比赛视频创建亮点，该模型旨在在足球比赛中识别重要事件，以创建比赛的亮点。我们基于更快的RCNN和Yolov5架构建立了模型，并注意到，对于我们用于训练速度更快的训练的数据量比Yolov5在检测比赛中的事件时表现要好，尽管它慢得多。在更快的RCNN中，使用RESNET50作为基本模型，可以更好地提供95.5％的基础准确性，而VGG16为92％，作为基本模型，我们的培训数据集完全胜过yolov5。我们使用了23分钟的原始视频测试，我们的模型可以将其减少到4:50分钟的亮点，以捕获比赛中几乎所有重要事件。
The research and data science community has been fascinated with the development of automatic systems for the detection of key events in a video. Special attention in this field is given to sports video analytics which could help in identifying key events during a match and help in preparing a strategy for the games going forward. For this paper, we have chosen Football (soccer) as a sport where we would want to create highlights for a given match video, through a computer vision model that aims to identify important events in a Soccer match to create highlights of the match. We built the models based on Faster RCNN and YoloV5 architectures and noticed that for the amount of data we used for training Faster RCNN did better than YoloV5 in detecting the events in the match though it was much slower. Within Faster RCNN using ResNet50 as a base model gave a better class accuracy of 95.5% as compared to 92% with VGG16 as base model completely outperforming YoloV5 for our training dataset. We tested with an original video of size 23 minutes and our model could reduce it to 4:50 minutes of highlights capturing almost all important events in the match.
