TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

Yu Chuan Huang, I. No Liao, Ching Hsuan Chen, Tsi-Ui Ik, Wen-Chih Peng

研究成果: Conference contribution同行評審

52 引文 斯高帕斯(Scopus)

摘要

Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. It is still challenging to recognize and position a high-speed and tiny ball accurately from an ordinary video. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and F1 -measure reach 99.7%, 97.3%, and 98.5%, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross-validation, and the precision, recall, and F1 -measure are 95.3%, 75.7%, and 84.3%, respectively.
原文English
主出版物標題16th IEEE International Workshop of Content-Aware Video Analysis (CAVA 2019) in conjunction with the 16th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2019)
發行者IEEE
頁數8
ISBN(電子)9781728109909
DOIs
出版狀態Published - 18 9月 2019
事件16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 - Taipei, Taiwan
持續時間: 18 9月 201921 9月 2019

出版系列

名字2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019

Conference

Conference16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
國家/地區Taiwan
城市Taipei
期間18/09/1921/09/19

指紋

深入研究「TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications」主題。共同形成了獨特的指紋。

引用此