计算机与数字工程2024，Vol.52Issue(10)：2955-2959,5.DOI:10.3969/j.issn.1672-9722.2024.10.018
基于深度学习的冰壶检测与轨迹追踪
Deep Learning-based Curling Detection and Trajectory Tracking
摘要
Abstract
Curling movement track capture can digitally restore the curling motion curve,which is not only conducive to the au-dience to better understand the competition situation,but also help players to make a judgment on the current situation.Robustly tracking a moving stone from curling video sequences is difficult because the stone is frequently hidden by the brushes held by the players and the players'bodies when players interact with stone.By optimizing the detection algorithm in deep learning,this paper realizes the capture of curling objects,the fast calibration of coordinates and the synthesis of curling trajectories.Multi-target track-ing method combined with curling motion characteristics and curling appearance.This paper also provides an actual curling dataset,which makes up for the lack of large-scale data set of curling.Compared with the existing methods,the experimental results show that the proposed method can efficiently perform tracking task with high accuracy and recall.关键词
冰壶/目标检测/目标追踪/轨迹特征Key words
curling/target detection/target tracking/trajectory feature分类
信息技术与安全科学引用本文复制引用
孙浩淼,李宗民,王向东,孙文洁..基于深度学习的冰壶检测与轨迹追踪[J].计算机与数字工程,2024,52(10):2955-2959,5.基金项目
国家重点研发计划(编号:2019YFF0301800) （编号:2019YFF0301800）
国家自然科学基金项目(编号:61379106) （编号:61379106）
山东省自然科学基金项目(编号:ZR2013FM036,ZR2015FM011)资助. （编号:ZR2013FM036,ZR2015FM011）
