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3D Scene Recovery based on Multiple Objects Tracking in Sport Videos

Volume 14, Number 3, March 2018, pp. 493-501
DOI: 10.23940/ijpe.18.03.p10.493501

Shihe Tiana, Ming Huangb, Yang Liuc, and Chengxin Lid

aTeaching and Research Department of Physical Education, Capital Normal University, Beijing, 100048, China
bHarbin No. 5 Middle School, Harbin, 150001, China
cHarbin Institute of Physical Education, Harbin, 150001, China
dThe Affiliated High School of Harbin Normal University, Harbin, 150001, China

(Submitted on December 29, 2017; Revised on January 30, 2018; Accepted on February 19, 2018)


Abstract:

This paper proposes a new method for estimating the player’s and ball’s 3D position information from monocular broadcast videos. For players, the homography between image and playfield is used to estimate their positions. By analyzing the geometry relation between the ball, its “virtual” shadow and camera position, we derive equations for estimating the flying ball’s 3D position. Moreover, we propose a method to predict the flying plane if it cannot be determined from images. This method designs a new cost function, which arrives at the minimum when the predicted flying plane is reasonable. This method has at least two merits. One is that it can estimate the flying ball’s position without referring to other objects with known height; the other is that only one assumption is made and the camera is in a fixed position. Experimental results are satisfying.

 

References: 15

  1. S. Choi, Y. Seo, H. Kim, K. S. Hong, “Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick”, International Conference on Image Analysis and Processing, pp. 196-203,1997
  2. S. Farin, P. Krabbe, “Robust Camera Calibration for Sport Videos Using Court Models”, SPIE Storage and Retrieval Methods and Applications for Multimedia, pp.80-91,2014
  3. H. Kim and K. S. Hong, “Robust Image Mosaicing of Soccer Videos Using Self-Calibration and Line Tracking”, Pattern Analysis & Applications, vol.4, pp.9-19,2011
  4. T. Kim, Y. Seo and K. S. Hong, “Physics-Based 3D Position Analysis of Soccer Ball from Monocular Image Sequence”, The International Conference on Computer Vision, pp.721-726,1998
  5. S. Nam, H. Kim and J. Kim, “Trajectory Estimation Based on Globally Consistent Homography”, International Conference Computer Analysis of Images and Patterns, pp. 214-221, 2013
  6. S. Iwase and H. Saito, “Tracking Soccer Players Based on Homography Among Multiple Views”, VCIP, pp.283-292, 2013
  7. S. Iwase, H. Saito, “Parallel Tracking of All Soccer Players by Integrating Detected Positions in Multiple View Images”, International Conference on Pattern Recognition, pp.751-754, 2014
  8. J. Ren, J. Orwell, G. A. Jones, M. Xu, “A General Framework for 3D Soccer Ball Estimation and Tracking”, IEEE International Conference on Image Processing, pp.1935-1938, 2014
  9. I. Reid, and A. North, “3D Trajectories from a Single Viewpoint Using Shadows”, British Machine Vision Conference, pp. 863-872, 1998
  10. H. Saito, N. Inamoto, S. Iwase, “Sports Scene Analysis and Visualization from Multiple-View Video”, “IEEE International Conference on Multimedia & Expo”, pp.1395-1398, 2014
  11. X. Tong, H. Lu, and Q. Liu, “An Effective and Fast Soccer Ball Detection and Tracking Method”, ICPR, pp.795-798. 2014
  12. T. Watanabe, M. Haseyama, H. Kitajima, “A Soccer Field Tracking Method with Wire Frame Model from TV Images”, International Conference Image Processing, vol.3, pp. 1633-1636, 2014
  13. M. Xu, J. Orwell, G. Jones, “Tracking Football Players with Multiple Cameras”, IEEE International Conference on Image Processing, 2909-2912, 2014
  14. A. Yamada, Y. Shirai, and J. Miura, “Tracking Players and a Ball in Video Image Sequence and Estimating Camera Parameters for 3D Interpretation of Soccer Games”, International Conference on Pattern Recognition, pp.303-306, 2012
  15. X. Yu, “3D Reconstruction and Enrichment of Broadcast Soccer Video”, ACM Multimedia, pp.260-263, 2014

 

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