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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68575
Volume:
10 |
10 |
Oct 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
Diploma in Artificial Intelligence & Machine Learning
Zeal Polytechnic Pune
A coaching system that uses a camera needs four main parts: finding people in the video, tracking each person, finding body points (pose), and checking sports movements. This paper reviews common methods for each part and compares their strengths and limits for real-time use with many athletes. We also look at existing sports analysis systems and datasets, list open problems such as blocked views, ID mix-ups, privacy and sport-specific rules, and suggest future work. At the end, we explain what this study means for Athletix AI, our proposed coaching system that is still being built. This is a review of published work. It has no new experiments, and all comparisons are descriptive and should be checked in the cited papers.
KEYWORDS
This survey reviewed the techniques needed for camera-based coaching support: person detection, multi-person tracking, pose estimation and movement analysis, along with existing sports systems and datasets. Good methods exist for each step, but reliable tracking of each athlete, sport-specific checking and ethical use are still open problems. These findings guide the design and testing plan of Athletix AI and show where more research is needed.
[1] Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, and Y. Sheikh, “OpenPose: Realtime multi-person 2D pose estimation using part affinity fields,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 1, pp. 172–186, 2021. [2] C. Lugaresi et al., “MediaPipe: A framework for building perception pipelines,” arXiv:1906.08172, 2019. [3] Y. Zhang et al., “ByteTrack: Multi-object tracking by associating every detection box,” in Proc. ECCV, 2022. [4] G. Thomas, R. Gade, T. B. Moeslund, P. Carr, and A. Hilton, “Computer vision for sports: Current applications and research topics,” Comput. Vis. Image Underst., vol. 159, pp. 3–18, 2017. [5] J. Wang, K. Qiu, H. Peng, J. Fu, and J. Zhu, “AI Coach: Deep human pose estimation and analysis for personalized athletic training assistance,” in Proc. ACM Multimedia, 2019.
Publication Date
10/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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