Real-time camera pose estimation for sports fields

被引:2
|
作者
Leonardo Citraro
Pablo Márquez-Neila
Stefano Savarè
Vivek Jayaram
Charles Dubout
Félix Renaut
Andrés Hasfura
Horesh Ben Shitrit
Pascal Fua
机构
[1] École Polytechnique Fédérale de Lausanne,Computer Vision Laboratory
[2] University of Bern,ARTORG Center for Computer Aided Surgery
[3] Second Spectrum Inc.,undefined
来源
关键词
Camera Pose Estimation; Camera Registration; Keypoints detection; Augmented Reality; Sport;
D O I
暂无
中图分类号
学科分类号
摘要
Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.To this end, we propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image by using a fully convolutional deep architecture.Our algorithm exploits both the field lines and the players’ image locations, assuming their ground plane positions to be given, to achieve accuracy and robustness that is beyond the current state of the art.We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.
引用
收藏
相关论文
共 50 条
  • [21] Research on Real-time Estimation for Human Pose
    Li, Beibei
    Zhao, Zhihong
    2013 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2013, : 301 - 305
  • [22] DESNet: Real-time human pose estimation for sports applications combining IoT and deep learning
    Huang, Rongbao
    Zhang, Bo
    Yao, Zhixin
    Xie, Bojun
    Guo, Jia
    ALEXANDRIA ENGINEERING JOURNAL, 2025, 112 : 293 - 306
  • [23] Real-Time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera
    Ye, Mao
    Shen, Yang
    Du, Chao
    Pan, Zhigeng
    Yang, Ruigang
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (08) : 1517 - 1532
  • [24] Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera
    Ye, Mao
    Yang, Ruigang
    2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, : 2353 - 2360
  • [25] Real-Time Human Pose Estimation: A Case Study in Algorithm Design for Smart Camera Networks
    Wu, Chen
    Aghajan, Hamid
    PROCEEDINGS OF THE IEEE, 2008, 96 (10) : 1715 - 1732
  • [26] Real-time Camera Pose Estimation Based on Planar Object Tracking for Augmented Reality Environment
    Lee, Ahr-Hyun
    Lee, Seok-Han
    Lee, Jae-Young
    Choi, Jong-Soo
    2012 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2012, : 516 - 517
  • [27] VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
    Mehta, Dushyant
    Sridhar, Srinath
    Sotnychenko, Oleksandr
    Rhodin, Helge
    Shafiei, Mohammad
    Seidel, Hans-Peter
    Xu, Weipeng
    Casas, Dan
    Theobalt, Christian
    ACM TRANSACTIONS ON GRAPHICS, 2017, 36 (04):
  • [28] A Real-Time Hand Pose Estimation System with Retrieval
    Hou, Guangdong
    Cui, Runpeng
    Zhang, Changshui
    2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS, 2015, : 1738 - 1744
  • [29] Accurate, robust, and real-time pose estimation of finger
    Department of Mechanical Engineering, University of Texas at Austin, Austin
    TX
    78712, United States
    J Dyn Syst Meas Control Trans ASME, 3
  • [30] Accurate, robust, and real-time pose estimation of finger
    Department of Mechanical Engineering, University of Texas at Austin, Austin
    TX
    78712, United States
    J Dyn Syst Meas Control Trans ASME, 3