@inproceedings{2e9adbfa5bcb4e2aa7373c49d28967b4,
title = "Event Detection in Soccer Video Based on Self-Attention",
abstract = "Sports big data technology has become an important technological means of modern sports competition. This paper realizes a novel soccer video event detection algorithm based on self-attention. It extracts key frames through self-attention mechanism, and then obtains the characteristics of time window level through NetVLAD network. Finally, each video clip is classified into 4 types of events (goals, red/yellow card, substitutions and others). The experimental results show that with the introduction of the self-attention mechanism, the classification accuracy on the SoccerNet data set has improved from 67.2% to 74.3%.",
keywords = "NetVLAD, event detection, key frame extraction, self-attention",
author = "Sifan Ma and En Shao and Xiang Xie and Wei Liu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 6th IEEE International Conference on Computer and Communications, ICCC 2020 ; Conference date: 11-12-2020 Through 14-12-2020",
year = "2020",
month = dec,
day = "11",
doi = "10.1109/ICCC51575.2020.9344896",
language = "English",
series = "2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1852--1856",
booktitle = "2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020",
address = "United States",
}