Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64236

SurveilFind :AI-Based Missing Person Detection and Intelligent Surveillance Monitoring System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64236

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64236

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.

Share this article:

Authors

shradha Davare

shradha Davare

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Author Certificate
Snehal Sanjay Dhamankar

Snehal Sanjay Dhamankar

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Co-Author Certificate
Sadika Sameer Mujawar

Sadika Sameer Mujawar

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Co-Author Certificate
Sakshi Ramesh Patole

Sakshi Ramesh Patole

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Co-Author Certificate
Tejal Tukaram Yamkar

Tejal Tukaram Yamkar

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Co-Author Certificate

Mentors

Sarita Ananda Chavan

Sarita Ananda Chavan

computer science and engineering

Ashokrao Mane Group of Institutions Vathar

Download Mentor Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

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

Artificial Intelligence Computer Vision Deep Learning Facial Recognition Missing Person Detection Smart Surveillance OpenCV FaceNet Surveillance Analytics.

Cite This Article

shradha Davare, Snehal Sanjay Dhamankar, Sadika Sameer Mujawar, Sakshi Ramesh Patole, Tejal Tukaram Yamkar, Sarita Ananda Chavan, "SurveilFind :AI-Based Missing Person Detection and Intelligent Surveillance Monitoring System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM64236

Full Article/Conclusion

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.

References

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.

Editorial & Review Transparency

Publication Date

5/28/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

3

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...