Research Article
Applying Convolutional Neural Network for Detecting Highlight Football Events
@INPROCEEDINGS{10.1007/978-3-030-93179-7_23, author={Tuan Hoang Viet Le and Hoang Thien Van and Hai Son Tran and Phat Kieu Nguyen and Thuy Thanh Nguyen and Thai Hoang Le}, title={Applying Convolutional Neural Network for Detecting Highlight Football Events}, proceedings={Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={ICCASA}, year={2022}, month={1}, keywords={Key frame Highlight football events Convolutional Neural Network (CNN) Highlight football events classification using CNN Wrongly-Validated Dataset Re-training (WVDR)}, doi={10.1007/978-3-030-93179-7_23} }- Tuan Hoang Viet Le
Hoang Thien Van
Hai Son Tran
Phat Kieu Nguyen
Thuy Thanh Nguyen
Thai Hoang Le
Year: 2022
Applying Convolutional Neural Network for Detecting Highlight Football Events
ICCASA
Springer
DOI: 10.1007/978-3-030-93179-7_23
Abstract
Automatic detection of videos with outstanding situations is a practical issue that needs to be studied in many events of different fields with common length and frequency of occurrence for instance: meetings, musicals, sports events that the user uploads regularly, one of the concerned areas is the highlights in football videos. The matches of the annual top leagues and between nations within federations form a huge database in need of different purposes in which requires the specific model assisting in extracting outstanding situations. Besides, building a reliable and accurate model requires an appropriate approach, a large amount of training data (diverse, accurate, clear data), which need to be assigned label correspondingly. The Convolutional Neural Network (CNN) was chosen as an approach to help building a smart system as a foundation, combined with a proposed new method for synthesizing results called the adaptive threshold towards specific data, along with the optimization model to draw a reliable conclusion. The work was proceeded on a video data set of the top four teams of the English Premier League (ELP) 2018–2019 and a randomly selected dataset on the Internet.
