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Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose

Marion Mundt¹, Steffi Colyer², Logan Wade²

¹UWA Tech & Policy Lab, The University of Western Australia, Crawley, Western Australia, Australia.

Scandinavian Journal of Medicine & Science in Sports

|July 10, 2024

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This summary is machine-generated.

Automating 2D sports video analysis requires accurate key event detection. Current methods using 3D algorithms or standard pose estimation struggle with accuracy, necessitating new algorithms for reliable performance insights.

Area of Science:

Sports Biomechanics
Computer Vision in Sports
Movement Analysis

Background:

Two-dimensional (2D) video analysis is crucial for sports training and performance evaluation.
Manual annotation of joint centers and kinematic parameters is time-consuming.
Automated methods using AI and computer vision offer potential efficiency gains.

Purpose of the Study:

To systematically analyze the automation of 2D video analysis workflows.
To investigate the applicability of 3D event detection algorithms to 2D video data.
To assess the agreement of 2D keypoints with 3D motion capture and the impact of event detection offsets.

Main Methods:

Utilized repeated measures limits of agreement to compare markerless and marker-based motion capture.

Keywords:

3D marker trajectory projection OpenPose knee angle running sampling frequency

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Evaluated a threshold-based event detection algorithm on 2D video at various sampling rates.

Compared 2D keypoints from pose estimation with projected 3D marker trajectories.

Main Results:

A minimum video sampling rate of 100 Hz is essential for accurate key event detection.
3D marker trajectory-based algorithms showed limited applicability for 2D video analysis.
Event misidentification (e.g., 20ms touchdown error) led to significant joint angle differences (up to 20°).

Conclusions:

Existing algorithms for 3D motion analysis are not directly transferable to 2D video.
Accurate key event detection is critical for reliable automated 2D video analysis.
Development of novel, de novo algorithms is necessary to enhance 2D video analysis pipelines.