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Measuring handball players trajectories using an automatically trained boosting algorithm.

Ricardo M L Barros1, Rafael P Menezes, Tiago G Russomanno

  • 1Department of Physical Education, University of Campinas, Campinas, Brazil. ricardo@fef.unicamp.br

Computer Methods in Biomechanics and Biomedical Engineering
|December 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an automated boosting detector for tracking handball players using morphological segmentation. The algorithm achieved 84% accuracy in trained games and improved player tracking efficiency.

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Area of Science:

  • Computer Vision
  • Sports Analytics
  • Machine Learning

Background:

  • Automated player tracking in sports is crucial for performance analysis.
  • Existing methods often require manual intervention or lack robustness.
  • Handball player tracking presents unique challenges due to fast motion and occlusions.

Purpose of the Study:

  • To propose and evaluate an automatically trained cascaded boosting detector for handball player tracking.
  • To assess the effectiveness of morphological segmentation in conjunction with boosting detectors.
  • To analyze the training dynamics and efficiency of the proposed algorithm.

Main Methods:

  • Development of an automatically trained cascaded boosting detector.
  • Integration of morphological segmentation for image region selection.
  • Evaluation of the algorithm on handball game footage.

Main Results:

  • The proposed method achieved 84% correct player detection on the training game and 74% on a different game.
  • Automatic morphological segmentation proved to be a fast and efficient region selection technique.
  • Training time initially increased with more figures but decreased with further additions.

Conclusions:

  • The developed boosting detector algorithm significantly improves automatic handball player tracking.
  • Morphological segmentation is an effective preprocessing step for boosting-based detection.
  • The automated training approach offers a viable solution for robust player tracking in sports.