Video system for determining the number of people
Abstract
The article investigates the effectiveness of using a video system to detect and track the movements of living objects, particularly people. A model for counting the number of people in a frame using the Arduino ESP_Camera hardware module and Python software has been developed. NumPy and OpenCV libraries have been used to process the video stream, which provide detection and tracking of movements in real time. The experimental results confirm that the system is able to accurately record the movements of people even in the presence of several moving objects, which indicates its practical effectiveness in real conditions. The proposed approach provides high accuracy in detecting movements in real-time video streams, which makes it promising for further application in the field of automated video surveillance.
References
Punn, N. S., Sonbhadra, S. K., Agarwal, S., & Rai, G. Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and DeepSORT techniques, 2020. arXiv preprint arXiv:2005.01385. https://arxiv.org/abs/2005.01385
Li, Y., Zhang, X., & Chen, D. (2018). CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, p. 1091-1100, doi: 10.1109/CVPR.2018.00120.
Chen, L., Wang, G. & Hou, G. Multi-scale and multi-column convolutional neural network for crowd density estimation. Multimed Tools Appl 80, 2021, p. 6661-6674, doi: 10.1007/s11042-020-10002-8.
Imran Ahmed, Misbah Ahmad, Awais Ahmad, Gwanggil Jeon. Top View Multiple People Tracking by Detection Using Deep SORT and YOLOv3 with Transfer Learning within 5G Infrastructure International. Journal of Machine Learning and Cybernetics, Volume 12, Issue 11/2021, p.3053-3067, doi: 10.1007/s13042-020-01220-5.
Song, Q., Wang, C., Jiang, Z., Wang, Y., Tai, Y., Wang, C., Li, J., Huang, F., & Wu, Y. Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p. 3365-3374. IEEE, doi: 10.1109/ICCV48922.2021.00335.
Nano, 2025, [Online]. Available: https://docs.arduino.cc/hardware/nano/.
Arduino and Stepper Motor Configurations, 2025, [Online]. Available: https://docs.arduino.cc/learn/electronics/stepper-motors/.
Nano ESP32, 2025, [Online]. Available: https://docs.arduino.cc/hardware/nano-esp32/.
Arun Kumar Jhapate, Sunil Malviya, Monika Jhapate, "Unusual Crowd Activity Detection using OpenCV and Motion Influence Map", 2nd International Conference on Data, Engineering, and Applications (IDEA), Bhopal, India, 2020, pp. 308 - 313, doi: 10.1109/IDEA49133.2020.9170704.
Yashika Tomar, Himanshu, Sanjana Devi, Husanpreet Kaur, "Human Motion Tracker using Open CV and Mediapipe", 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India, 2023, pp. 1199 - 1204, doi: 10.1109/ICIMIA60377.2023.10425865.
O. Pereverziev, K. Trapezon, "Research of software algorithms for tracking the movement of objects in electronic security systems". Vcheni zapysky TNU imeni Vernadskogo. Seriya Tekhnichni nauky, Vol. 33 (72), № 6, p. 288-293, 2022, doi: 10.32782/2663-5941/2022.6/47.
Narendra Kumar Rao B, Nagendra Panini Challa, E S Phalguna Krishna, S. Sreenivasa Chakravarthi, "Facial Landmarks Detection System with OpenCV Mediapipe and Python using Optical Flow (Active) Approach", 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 92 - 96, doi: 10.1109/ICACITE57410.2023.10182585.
S. Damotharasamy, "Approach to model human appearance based on sparse representation for human tracking in surveillance," IET Image Processing, 14(11), 2383-2394, 2020, doi: 10.1049/iet-ipr.2018.5961
A. B. Sadkhan, S. R. Talebiyan, and N. Farzaneh, "Detection and Moving Object Tracking in images using an improved Kallman Filter (KF) by an Invasive weed optimization algorithm", International Conference on Advanced Computer Applications (ACA), Maysan, Iraq, 2021, pp. 186-192, doi: 10.1109/ACA52198.2021.9626817.
K.M. Abughalieh, S.G. Alawneh, "Predicting Pedestrian Intention to Cross the Road," IEEE Access, 8, 72558-72569, 2020, doi: 10.1109/ACCESS.2020.2987777.
Copyright (c) 2025 Уляна Дзелендзяк, Данило Нікульшин (Автор)

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