Tracking the soccer ball using multiple fixed cameras

Jinchang Ren*, James Orwell, Graeme A. Jones, Ming Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


This paper demonstrates innovative techniques for estimating the trajectory of a soccer ball from multiple fixed cameras. Since the ball is nearly always moving and frequently occluded, its size and shape appearance varies over time and between cameras. Knowledge about the soccer domain is utilized and expressed in terms of field, object and motion models to distinguish the ball from other movements in the tracking and matching processes. Using ground plane velocity, longevity, normalized size and color features, each of the tracks obtained from a Kalman filter is assigned with a likelihood measure that represents the ball. This measure is further refined by reasoning through occlusions and back-tracking in the track history. This can be demonstrated to improve the accuracy and continuity of the results. Finally, a simple 3D trajectory model is presented, and the estimated 3D ball positions are fed back to constrain the 2D processing for more efficient and robust detection and tracking. Experimental results with quantitative evaluations from several long sequences are reported.

Original languageEnglish
Pages (from-to)633-642
Number of pages10
JournalComputer Vision and Image Understanding
Issue number5
Publication statusPublished - May 2009


  • 3D vision
  • Domain knowledge modeling
  • Motion analysis
  • Sports analysis
  • Trajectory modeling
  • Video signal processing


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