Authors

P. Paul Aditya

Department of Computer Science and Engineering, University College of Engineering Kakinada (A), Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, India

Abstract

The increasing use of camera-based monitoring in public spaces requires person detection systems that can operate across changes in illumination, viewpoint, background complexity, crowd density, and object scale. This paper presents an intelligent person detection framework based on YOLOv11m for open-area surveillance. A custom dataset containing more than 6,000 annotated outdoor images was prepared to represent diverse scene conditions and person distributions. The images were used to train the YOLOv11m detector, which performs person localization through bounding boxes and confidence scores in a single-stage detection pipeline. The trained model was integrated with a Streamlit application to provide image upload, preprocessing, detection visualization, performance reporting, and optional person-count and occupancy information. Evaluation used Precision, Recall, mAP@50, Box Loss, and inference time. The reported final evaluation values were 89.41% Precision, 87.22% Recall, 88.21% mAP@50, and 0.499 Box Loss. A representative application inference reported demonstration environment. Training curves showed progressive improvement in Precision, Recall, and mAP@50 with a corresponding reduction in Box Loss. The resulting framework provides an integrated approach for person detection and performance analysis in varied outdoor public-space scenes.

Keywords

YOLOv11m Person Detection Object Detection Deep Learning Public-Space Surveillance Real-Time Detection Outdoor Surveillance Computer Vision

Citation of this Article

P. Paul Aditya. (2026). An Intelligent Person Detection System for Public Spaces Using YOLOv11. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(9), 49-56. Article DOI: https://doi.org/10.47001/JAIET/2026.309005 

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. P. Chaudhari, A. Kulkarni, and R. Patil, “Fundamentals, algorithms, and technologies of occupancy detection in smart buildings: A comprehensive review,” IEEE Access, vol. 12, pp. 45678–45705, 2024.
  2. H. Xu, Y. Wang, and Z. Li, “A survey on occupancy perception for autonomous driving,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 1450–1472, 2024.
  3. Y. Zhang, M. Chen, and T. Liu, “Vision-based 3D occupancy prediction: A review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 1, pp. 112–130, 2025.
  4. L. Deng, J. Li, and S. Zhou, “Deep learning in crowd counting: A survey,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 4, pp. 2150–2170, 2024.
  5. M. Alhawsawi and K. Deb, “A comprehensive survey of crowd density estimation and counting approaches,” IEEE Access, vol. 13, pp. 22110–22135, 2025.
  6. H. Elkhoukhi, A. Saad, and M. Benammar, “Occupancy sensing technologies in smart buildings: A review,” IEEE Sensors Journal, vol. 24, no. 8, pp. 10234–10256, 2024.
  7. E. El-Kenawy, M. Ibrahim, and A. Khodadadi, “Smart room occupancy detection using neural networks and transfer learning: A review,” IEEE Access, vol. 13, pp. 33412–33429, 2025.
  8. N. Francis and R. James, “Thermal imaging-based occupancy detection: Methods and privacy considerations,” IEEE Sensors Journal, vol. 25, no. 2, pp. 1560–1578, 2025.
  9. J. Jeong, H. Kim, and S. Lee, “Privacy-preserving occupancy counting: A review of labeling-free and low-resolution approaches,” IEEE Internet of Things Journal, vol. 12, no. 3, pp. 1987–2005, 2025.
  10. M. Valverde, A. Ortiz, and F. Garcia, “Deep learning-based 3D object and occupancy detection: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 1, pp. 540–558, 2025.
  11. C. Tian, X. Zhang, and L. Sun, “Recent advances in deep learning: Models, optimization, and deployment,” IEEE Access, vol. 13, pp. 45000–45025, 2025.
  12. A.Alharthi and M. Hussain, “Advances in crowd counting and density estimation using deep learning,” IEEE Access, vol. 11, pp. 98765–98790, 2023.
  13. R. Kumar and P. Singh, “Vision-based building occupancy estimation: Current trends and future directions,” IEEE Access, vol. 12, pp. 60012–60030, 2024.
  14. S. Wong and K. Lim, “Vision-based detection systems for smart infrastructure: A review,” IEEE Sensors Journal, vol. 23, no. 15, pp. 17045–17063, 2023.
  15. Y. Sun, L. Zhao, and Q. Huang, “Sensor fusion techniques for indoor occupancy estimation: A review,” IEEE Internet of Things Journal, vol. 11, no. 9, pp. 7805–7822, 2024.
  16. T. Rahman and J. Park, “Multi-modal deep learning for occupancy detection in smart buildings,” IEEE Access, vol. 12, pp. 88221–88240, 2024.
  17. D. Chen, F. Xu, and H. Zhao, “Edge deployment of deep learning models for smart building applications: A survey,” IEEE Transactions on Industrial Informatics, vol. 21, no. 1, pp. 302–320, 2025.
  18. K. Patel and R. Mehta, “Real-time human detection and counting: A review of YOLO-based approaches,” IEEE Access, vol. 11, pp. 112340–112360, 2023.
  19. J. Li, W. Wu, and X. Tang, “Vision-based occupancy detection and energy optimization in buildings: A review,” IEEE Transactions on Automation Science and Engineering, vol. 22, no. 2, pp. 890–905, 2025.
  20. P. Singh and V. Sharma, “Privacy-aware deep learning methods for camera-based indoor occupancy detection,” IEEE Access, vol. 13, pp. 72110–72128, 2025.