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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper ID:IJSREM68575

Computer Vision Techniques for Multi-Athlete Detection, Pose Tracking and Real-Time Coaching Support

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68575

Volume:

10 |

10 |

Oct 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

PNS

Prajakta Nitin Shirsat

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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SSK

Siddhi Sameer Khambe

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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GSS

Gayatri Santram Sathe

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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PMS

Payal Mohan Sulkar

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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ISK

Ishwari Shivaji Kadam

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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Mentors

PAM

Prof. Aboli More

Diploma in Artificial Intelligence & Machine Learning

Zeal Polytechnic Pune

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Abstract

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

survey pose estimation multi-object tracking person detection sports analytics real-time coaching.

Cite This Article

Prajakta Nitin Shirsat, Siddhi Sameer Khambe, Gayatri Santram Sathe, Payal Mohan Sulkar, Ishwari Shivaji Kadam, Prof. Aboli More, "Computer Vision Techniques for Multi-Athlete Detection, Pose Tracking and Real-Time Coaching Support," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-3, Oct 2026.

Full Article/Conclusion

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.

References

[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.

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Publication Date

10/31/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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