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VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics

Gonzalo Garrido-Lopez, Luis F. Gomez, Julian Fierrez, Aythami Morales, Ruben Tolosana, Javier Rueda, Enrique Navarro

TL;DR

The proposed VideoRun2D based on MoveNet core seems to be a helpful tool for evaluating sprint kinematics in some scenarios, but the observed precision of this first version of VideoRun2D as a markerless sprint analysis system may not be yet enough for highly demanding applications.

Abstract

Sprinting is a determinant ability, especially in team sports. The kinematics of the sprint have been studied in the past using different methods specially developed considering human biomechanics and, among those methods, markerless systems stand out as very cost-effective. On the other hand, we have now multiple general methods for pixel and body tracking based on recent machine learning breakthroughs with excellent performance in body tracking, but these excellent trackers do not generally consider realistic human biomechanics. This investigation first adapts two of these general trackers (MoveNet and CoTracker) for realistic biomechanical analysis and then evaluate them in comparison to manual tracking (with key points manually marked using the software Kinovea). Our best resulting markerless body tracker particularly adapted for sprint biomechanics is termed VideoRun2D. The experimental development and assessment of VideoRun2D is reported on forty sprints recorded with a video camera from 5 different subjects, focusing our analysis in 3 key angles in sprint biomechanics: inclination of the trunk, flex extension of the hip and the knee. The CoTracker method showed huge differences compared to the manual labeling approach. However, the angle curves were correctly estimated by the MoveNet method, finding errors between 3.2° and 5.5°. In conclusion, our proposed VideoRun2D based on MoveNet core seems to be a helpful tool for evaluating sprint kinematics in some scenarios. On the other hand, the observed precision of this first version of VideoRun2D as a markerless sprint analysis system may not be yet enough for highly demanding applications. Future research lines towards that purpose are also discussed at the end: better tracking post-processing and user- and time-dependent adaptation.

VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics

TL;DR

The proposed VideoRun2D based on MoveNet core seems to be a helpful tool for evaluating sprint kinematics in some scenarios, but the observed precision of this first version of VideoRun2D as a markerless sprint analysis system may not be yet enough for highly demanding applications.

Abstract

Sprinting is a determinant ability, especially in team sports. The kinematics of the sprint have been studied in the past using different methods specially developed considering human biomechanics and, among those methods, markerless systems stand out as very cost-effective. On the other hand, we have now multiple general methods for pixel and body tracking based on recent machine learning breakthroughs with excellent performance in body tracking, but these excellent trackers do not generally consider realistic human biomechanics. This investigation first adapts two of these general trackers (MoveNet and CoTracker) for realistic biomechanical analysis and then evaluate them in comparison to manual tracking (with key points manually marked using the software Kinovea). Our best resulting markerless body tracker particularly adapted for sprint biomechanics is termed VideoRun2D. The experimental development and assessment of VideoRun2D is reported on forty sprints recorded with a video camera from 5 different subjects, focusing our analysis in 3 key angles in sprint biomechanics: inclination of the trunk, flex extension of the hip and the knee. The CoTracker method showed huge differences compared to the manual labeling approach. However, the angle curves were correctly estimated by the MoveNet method, finding errors between 3.2° and 5.5°. In conclusion, our proposed VideoRun2D based on MoveNet core seems to be a helpful tool for evaluating sprint kinematics in some scenarios. On the other hand, the observed precision of this first version of VideoRun2D as a markerless sprint analysis system may not be yet enough for highly demanding applications. Future research lines towards that purpose are also discussed at the end: better tracking post-processing and user- and time-dependent adaptation.
Paper Structure (15 sections, 11 figures)

This paper contains 15 sections, 11 figures.

Table of Contents

  1. Introduction
  2. Proposed System
  3. VideoRun2D: Architecture
  4. VideoRun2D: Modules
  5. Video pre-processing:
  6. Tracking:
  7. Tracking post-processing:
  8. Biomechanical features generation:
  9. Statistical analysis
  10. Experiments
  11. Dataset
  12. Ground truth based on manual labeling (Kinovea):
  13. Results
  14. Discussion and Conclusion
  15. Acknowledgments.

Figures (11)

  • Figure 1: Block diagram of the proposed VideoRun2D system for joint angles estimation, which employs five processing modules. (In this particular example, the tracked skeleton was obtained with the MoveNet tracker core.) See the example video at: https://github.com/BiDAlab/VideoRun2D
  • Figure 2: Graphical example of the correction of tracking errors using support vector regression, both before (on the left) and after (on the right) post-processing.
  • Figure 3: Graphical description of the joint angles variables including maximal range of motion of each joint during sprinting according to the mentioned literature on the discussion section.
  • Figure 4: Example of the different stages of a stride for an individual in our dataset (which consists of 5 individuals, 40 sprints, 240 strides in total). See the example video at: https://github.com/BiDAlab/VideoRun2D
  • Figure 5: Data acquisition sketch.
  • ...and 6 more figures