Sift-based multi-view cooperative tracking for soccer video

This paper presents a SIFT-based multi-view cooperative tracking scheme for multiple player tracking in soccer games. We assume that future sports events will be captured by an array of fixed high-definition cameras which provide multi-view video sequences. The imagery will then be used to provide a free-viewpoint networked experience. In this work, SIFT features are used to extract the interview and inter-frame correlation among related views. Hence, accurate 3D information of each player can be efficiently utilized for real time multiple player tracking. By sharing the 3D information with all cameras and exploiting the perspective diversity of the multi-camera system, occlusion problems can be solved effectively. The extracted 3D information improves the average reliability of tracking by more than 10% when compared to SIFT-based 2D tracking.

[1]  Haopeng Li,et al.  Rate-distortion-optimized content-adaptive coding for immersive networked experience of sports events , 2011, 2011 18th IEEE International Conference on Image Processing.

[2]  Robert T. Collins,et al.  Mean-shift blob tracking through scale space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[4]  Harpreet S. Sawhney,et al.  Real-time wide area multi-camera stereo tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Roland Mech,et al.  A noise robust method for segmentation of moving objects in video sequences , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Huiyu Zhou,et al.  Object tracking using SIFT features and mean shift , 2009, Comput. Vis. Image Underst..

[8]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..