arXiv:2606.09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage. The older marker-based motion capture systems evolving to the modern accessible and adaptable markerless deep learning approaches, this survey explores the foundational architectures, which balance precision and efficiency. We also compare algorithmic fr
Advances in AI, particularly deep learning and computer vision, are enabling practical real-time human pose estimation, making previously complex motion tracking widely accessible.
This development moves sophisticated athletic performance analysis from specialized labs to everyday sports, directly impacting training, injury prevention, and competitive advantage across numerous disciplines.
The ability to accurately track and analyze human movement in real-world sporting environments without intrusive markers opens new avenues for personalized coaching, automated feedback systems, and accessible performance optimization tools.
- · Sports Technology Companies
- · Professional Sports Teams
- · Individual Athletes
- · Wearable Technology Manufacturers
- · Traditional Marker-Based Motion Capture Systems
- · Sports Analysts reliant solely on manual observation
Widespread adoption of AI-powered motion analysis in amateur and professional sports for training and performance improvement.
Increased competition among sports tech companies to integrate advanced AI analytics, leading to more sophisticated and affordable solutions.
The data generated from widespread motion tracking could inform biomechanical research, leading to new insights into human movement and injury mechanisms.
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