Rugby event detection in broadcast videos based on visual features using deep learning

Abstract

A sports play event is an athletic activity that is performed by multiple players during a sporting event. Sports Event Detection is a challenging task in the domain of sports video analytics. Numerous attempts were made to detect events occurring in sports such as soccer, basketball, and cricket. Our primary objective in this research is to detect events in a Rugby sports video. In comparison to other sports, this one is more difficult due to the sport’s chaotic nature. As a result, very little research is conducted on the Rugby sport. The Rugby Events Dataset is presented in this paper as a benchmark dataset for event detection in rugby. It contains videos with temporal annotations for events as well as images with bounding box annotations for the same. Nevertheless, using deep learning and computer vision techniques, this research was able to successfully train on this dataset and detect rugby events as well as temporally localize those events in broadcasted videos. A simple classification model is used to distinguish between sports fields and other scenes in these videos, while an object detection model is used to identify sporting events. Whereas current object detection models are used to detect objects, this research demonstrates that these models can be extended to detect sports events and still produce satisfactory results. Combining tracking with object detection models increased our accuracy of localizing events in the temporal domain even further. This project has released a Sports Event Detection Framework which can be deployed in any machine. The RugbyEvents dataset is publicly available in