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Algorithms
Volume 19
Issue 6
10.3390/a19060464
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Proof of Concept for a Deep-Learning Computer-Vision System to Quantify External Load in Basketball: Comparison with Local Positioning Systems

by
Athanasios Chatzinikolaou
1,
Ioannis Kansizoglou
2,
Antonios Gasteratos
3,
Georgios Pistikos
4,
Ioannis Papavasilopoulos
4,
Panagiotis Kaddas
4,
Dimitrios Pantazis
1,
Panagiotis Aggelakis
1,
Dimitrios Balampanos
1,
Alexandros Dendrinos
1,
Stavros Moutsis
3,
Sarantis Antoniou
3,
Panagiotis Foteinakis
1,
Konstantinos Margonis
1,
Nikolaos Zaras
1,
Alexandra Avloniti
1,
Christos Kazantzis
1,
Athanasios Kaltsos
1,
Georgios Pavlidis
5 and
Christos Kokkotis
2,*
1
Department of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, GR-69100 Komotini, Greece
2
Department of Occupational Therapy, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, GR-69100 Komotini, Greece
3
Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace, GR-67132 Xanthi, Greece
4
InDigital S.A., Dirfis 31, GR-15235 Athens, Greece
5
Ιnstitute for Language and Speech Processing, Athena Research Center, University Campus at Kimmeria, GR-67100 Xanthi, Greece
*
Author to whom correspondence should be addressed.
Algorithms 2026, 19(6), 464; https://doi.org/10.3390/a19060464
Submission received: 11 May 2026 / Revised: 3 June 2026 / Accepted: 5 June 2026 / Published: 7 June 2026
(This article belongs to the Special Issue Crowdsourcing, Human-AI Interaction, and the Future of Digital Platforms)
Versions Notes

Abstract

Background: Monitoring external load in team sports is essential for performance optimization, injury prevention, and individualized training prescription. Although Local Positioning Systems (LPS) are widely used for indoor athlete tracking, they require wearable devices and specialized infrastructure. Recent advances in artificial intelligence and computer vision allow markerless athlete tracking; however, their validity for basketball remains insufficiently explored. Objective: To evaluate the validity of a deep-learning multi-camera computer-vision system for quantifying external-load variables in basketball compared with a commercial LPS. Methods: The framework integrated fisheye video acquisition, player detection, and pose estimation using YOLOv11x-Pose and player re-identification through ResNet-50 and FAISS similarity search. Positional data were transformed into real-world court coordinates to derive distance, acceleration, deceleration, player load, and average speed metrics. Outputs were compared with measurements obtained from Kinexon LPS. Results: Strong correlations were observed for total distance (r = 0.92), acceleration counts (r = 0.90), deceleration counts (r = 0.92), and player load (r = 0.81), while average speed showed a moderate-to-strong correlation (r = 0.66). ICC and Bland–Altman analyses indicated agreement between systems. Conclusions: The proposed computer-vision system demonstrated high agreement with LPS, supporting its use as a valid, non-invasive, and scalable solution for external load monitoring in basketball.
Keywords: computer vision; deep learning; local positioning system; external load; pose estimation; YOLO; sports analytics; artificial intelligence computer vision; deep learning; local positioning system; external load; pose estimation; YOLO; sports analytics; artificial intelligence

Share and Cite

MDPI and ACS Style

Chatzinikolaou, A.; Kansizoglou, I.; Gasteratos, A.; Pistikos, G.; Papavasilopoulos, I.; Kaddas, P.; Pantazis, D.; Aggelakis, P.; Balampanos, D.; Dendrinos, A.; et al. Proof of Concept for a Deep-Learning Computer-Vision System to Quantify External Load in Basketball: Comparison with Local Positioning Systems. Algorithms 2026, 19, 464. https://doi.org/10.3390/a19060464

AMA Style

Chatzinikolaou A, Kansizoglou I, Gasteratos A, Pistikos G, Papavasilopoulos I, Kaddas P, Pantazis D, Aggelakis P, Balampanos D, Dendrinos A, et al. Proof of Concept for a Deep-Learning Computer-Vision System to Quantify External Load in Basketball: Comparison with Local Positioning Systems. Algorithms. 2026; 19(6):464. https://doi.org/10.3390/a19060464

Chicago/Turabian Style

Chatzinikolaou, Athanasios, Ioannis Kansizoglou, Antonios Gasteratos, Georgios Pistikos, Ioannis Papavasilopoulos, Panagiotis Kaddas, Dimitrios Pantazis, Panagiotis Aggelakis, Dimitrios Balampanos, Alexandros Dendrinos, and et al. 2026. "Proof of Concept for a Deep-Learning Computer-Vision System to Quantify External Load in Basketball: Comparison with Local Positioning Systems" Algorithms 19, no. 6: 464. https://doi.org/10.3390/a19060464

APA Style

Chatzinikolaou, A., Kansizoglou, I., Gasteratos, A., Pistikos, G., Papavasilopoulos, I., Kaddas, P., Pantazis, D., Aggelakis, P., Balampanos, D., Dendrinos, A., Moutsis, S., Antoniou, S., Foteinakis, P., Margonis, K., Zaras, N., Avloniti, A., Kazantzis, C., Kaltsos, A., Pavlidis, G., & Kokkotis, C. (2026). Proof of Concept for a Deep-Learning Computer-Vision System to Quantify External Load in Basketball: Comparison with Local Positioning Systems. Algorithms, 19(6), 464. https://doi.org/10.3390/a19060464

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