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SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks.

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  • 1Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy.

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Summary
This summary is machine-generated.

This study introduces swimmerNET, a marker-less pose estimation system for swimmers. It uses computer vision and neural networks for accurate swimming technique analysis, improving upon traditional methods.

Keywords:
athlete’s performance measurementsfully convolutional neural networksswimmers pose estimationvision-based underwater measurements

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Area of Science:

  • Sports Science
  • Computer Vision
  • Biomechanical Analysis

Background:

  • Manual video analysis of swimming performance is time-consuming and error-prone.
  • Existing automated techniques have limitations in style applicability and accuracy due to visual disturbances.
  • Wearable sensors and optical markers can be intrusive and affect athlete performance.

Purpose of the Study:

  • To develop a novel, marker-less 2D pose estimation system for swimmers.
  • To improve the accuracy and efficiency of swimming technique analysis.
  • To provide a non-intrusive solution for evaluating athletic performance.

Main Methods:

  • Development of swimmerNET, a system combining computer vision algorithms and fully convolutional neural networks.
  • Utilizing a single 8 Mpixel wide-angle camera for data acquisition.
  • Marker-less 2D pose estimation of swimmers during exercise.

Main Results:

  • The swimmerNET system achieved adequate measurement accuracy.
  • Demonstrated an average error of approximately 1 mm.
  • Showcased a standard deviation (worst-case scenario) of approximately 10 mm.
  • Successfully tested on athletes with diverse physical characteristics and swimming techniques.

Conclusions:

  • SwimmerNET offers a reliable and accurate marker-less solution for swimmer pose estimation.
  • The system overcomes limitations of previous automated and sensor-based methods.
  • It provides a valuable tool for coaches to analyze and improve swimming techniques efficiently.