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Intelligent filtering by semantic importance for single-view 3d reconstruction from snooker video

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
  • Philip A. Legg
  • Matthew L. Parry
  • David H. S. Chung
  • Richard M. Jiang
  • Adrian Morris
  • Iwan W. Griffiths
  • David Marshall
  • Min Chen
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Publication date2011
Host publication2011 18th IEEE International Conference on Image Processing
PublisherIEEE
Pages2385-2388
Number of pages4
ISBN (electronic)9781457713033, 9781457713026
ISBN (print)9781457713040
<mark>Original language</mark>English

Publication series

Name2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
ISSN (Print)1522-4880

Abstract

In this paper we investigate the challenge of 3D reconstruction from Snooker video data. We propose a system pipeline for intelligent filtering based on semantic importance in Snooker. The system can be divided into table detection and correction, followed by ball detection, classification and tracking. It is apparent from previous work that there are several challenges presented here. Firstly, previous methods tend to use a fixed top-down camera mounted above the table. To capture a full table view from this is challenging due to space limitations above the table. Instead, we capture video data from a tripod and correct the viewpoint through processing. Secondly, previous methods tend to simply detect the balls without considering other interfering objects such as player and cue. This becomes even more apparent when the player strikes the cue ball. Our intelligent filtering avoids such issues to give accurate 3D table reconstruction.