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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62440
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
ABSTRACT: This paper presents a Wireless Surveillance Robot with Real-Time Face Detection designed for remote monitoring and security applications. The system uses an ESP32 and NRF24L01 for wireless control, while a Raspberry Pi Zero 2W processes commands and controls robot movement. A Raspberry Pi Camera Module v2 mounted on a servo motor provides live video streaming with a wider field of view. The system uses OpenCV for real-time face detection and identification. Developed using Python and embedded technologies, the robot offers reliable control, stable video transmission, and efficient surveillance, making it suitable for security and remote monitoring applications.
KEYWORDS
This project successfully presents a Wireless Surveillance Robot with Real-Time Face Detection for smart monitoring applications. The system combines ESP32, NRF24L01, and Raspberry Pi Zero 2W to achieve wireless control, live video streaming, and real-time face detection using OpenCV. The robot provides reliable movement, wide camera coverage, and efficient monitoring capabilities. Overall, the system offers a low-cost, practical, and scalable solution for security surveillance and remote monitoring applications.
[1] P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2001. [2] OpenCV Documentation, "Haar Cascade Classifier," Available: https://docs.opencv.org/ [3] Nordic Semiconductor, "nRF24L01+ Single Chip 2.4GHz Transceiver Product Specification," Rev 1.0, 2008. [4] Espressif Systems, "ESP32 Technical Reference Manual," Version 5.0, 2023.
Publication Date
5/3/2026
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.
Funding Information
No funding information available for this research.
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