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
Citation:
Jayasuriya, D.P. (2022). Rugby event detection in broadcast videos based on visual features using deep learning [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21545