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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64236
Volume:
10 |
5 |
May 2026
Pages:
1-8
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.

computer science and engineering
Ashokrao Mane Group of Institutions Vathar

computer science and engineering
Ashokrao Mane Group of Institutions Vathar

computer science and engineering
Ashokrao Mane Group of Institutions Vathar

computer science and engineering
Ashokrao Mane Group of Institutions Vathar

computer science and engineering
Ashokrao Mane Group of Institutions Vathar

computer science and engineering
Ashokrao Mane Group of Institutions Vathar
The rapid rise in missing person incidents has created serious challenges for public safety organizations and law enforcement departments. Conventional surveillance approaches mainly depend on manual monitoring of CCTV footage, public notices, and time-consuming investigation procedures, which often delay identification and recovery processes. This paper introduces SurveilFind, an AI-enabled surveillance platform designed to automate missing person detection using Computer Vision and Deep Learning technologies. The proposed system continuously monitors live CCTV streams and identifies individuals through advanced facial recognition techniques. Deep Learning models including FaceNet, DeepFace, MTCNN, and OpenCV are integrated to perform face detection, feature extraction, and identity matching operations. Detected facial patterns are compared with records stored in a centralized missing-person database using embedding similarity algorithms
KEYWORDS
SurveilFind is an AI-powered Missing Person Detection and Intelligent Surveillance Monitoring System developed using React, Python, Deep Learning, and Computer Vision technologies. The platform successfully automated CCTV surveillance analysis, facial recognition, and intelligent alert generation through real-time video monitoring. Integration of FaceNet, DeepFace, MTCNN, and OpenCV improved recognition efficiency and enabled accurate identification of missing individuals. The software-oriented architecture minimized dependency on expensive hardware infrastructure while improving scalability, flexibility, and monitoring performance. The project demonstrates the practical application of Artificial Intelligence and Computer Vision in enhancing public safety, improving surveillance efficiency, and reducing manual monitoring efforts through intelligent automation. With future technological advancements and scalability improvements, SurveilFind has the potential to evolve into a robust smart surveillance platform suitable for large-scale security and public safety environments.
1. Schroff, F., Kalenichenko, D., & Philbin, J. “FaceNet: A Unified Embedding for Face Recognition and Clustering,” CVPR, 2015. 2. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” CVPR, 2014. 3. Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. “Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks,” IEEE Signal Processing Letters, 2016. 4. Viola, P., & Jones, M. “Rapid Object Detection using a Boosted Cascade of Simple Features,” CVPR, 2001. 5. Deng, J., Guo, J., Xue, N., & Zisserman, A. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” CVPR, 2019. 6. OpenCV Documentation. “Face Detection and Recognition using OpenCV.” 7. TensorFlow Documentation. “Deep Learning Framework for AI Applications.” 8. Sandberg, D. “Face Recognition using TensorFlow and FaceNet,” GitHub Repository, 2018. 9. King, D. E. “Dlib-ML: A Machine Learning Toolkit,” Journal of Machine Learning Research, 2009. 10. Parkhi, O. M., Vedaldi, A., & Zisserman, A. “Deep Face Recognition,” BMVC, 2015.
Publication Date
5/28/2026
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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