Real Time Suspicious Activity Detection in Surveillance Camera Using YOLO V12

DOI

10.2991/978-94-6239-616-6_58How to use a DOI?

Keywords

Abnormal behavior detection; transfer learning; surveillance systems; activity tracking; automated reporting; crime prevention; deep learning; security monitoring; real-time analysis; situational awareness

Abstract

The rapid rise in criminal activity has highlighted the necessity of intelligent surveillance systems that can detect threats proactively and monitor in real time. Typical drawbacks of traditional deep learning models for identifying aberrant behavior include low accuracy, high computational expense, and limited ability to adjust to changing conditions. This study proposes an improved real-time suspicious activity detection framework that combines a Feature Pyramid Network (FPN) with the YOLOv12 architecture in order to overcome these difficulties. In order to accurately detect small and complex objects in a variety of environmental conditions, the FPN enhances YOLOv12’s multi-scale feature representation capability. The proposed system effectively identifies, monitors, and analyzes human activities within surveillance areas while ensuring rapid processing and minimized computational demands. An automated reporting module produces comprehensive analytical summaries to aid security personnel in swift decision-making. Experimental evaluations indicate that the proposed YOLOv12 with FPN model surpasses traditional detection methods regarding accuracy, inference speed, and robustness. This framework provides a scalable, resource-efficient, and dependable solution for contemporary surveillance applications in both public and private sectors.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Thilagavathi
AU  - R. Kaushic
AU  - P. Suganthan
AU  - N. Sudharshan
PY  - 2026
DA  - 2026/03/31
TI  - Real Time Suspicious Activity Detection in Surveillance Camera Using YOLO V12
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 769
EP  - 788
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_58
DO  - 10.2991/978-94-6239-616-6_58
ID  - Thilagavathi2026
ER  -