Dynamic Hyper-Contextual Surveillance Network with Autonomous Anomaly Classification

Vivitha Nisanthini R. S., Sowmiya Sree C., Shyam Sundar M.

2025

Abstract

The proposed Dynamic Hyper-Contextual Surveillance Network with Autonomous Anomaly Classification operates in the computer vision and artificial intelligence domain, focusing on real-time security monitoring and threat detection. Traditional surveillance systems struggle with high false alarm rates, delayed responses, and an inability to autonomously classify threats, making them inefficient in high-risk environments. Our system addresses these limitations by integrating YOLO-based object detection for weapon identification, Haar cascades for facial recognition, and AI-driven emotion detection to classify potential threats accurately. Unlike conventional systems that rely solely on manual intervention, our approach enhances real-time decision-making with automated alerts and an inbuilt emergency response mechanism. The system leverages edge AI and cloud processing for scalability, ensuring low-latency performance even with multiple cameras. Experimental results show a 30% improvement in threat detection accuracy and a 40% reduction in response time, validating the efficiency of our approach. By integrating context-aware anomaly detection, an emergency SOS feature, and intelligent alert mechanisms, the system significantly improves security response efficiency, making it highly suitable for high-security government and military applications.

Paper Citation

in Harvard Style

S. V., C. S. and M. S. (2025). Dynamic Hyper-Contextual Surveillance Network with Autonomous Anomaly Classification. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 822-829. DOI: 10.5220/0013906400004919

in Bibtex Style

@conference{icrdicct`2525,
author={Vivitha S. and Sowmiya C. and Shyam M.},
title={Dynamic Hyper-Contextual Surveillance Network with Autonomous Anomaly Classification},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={822-829},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013906400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Dynamic Hyper-Contextual Surveillance Network with Autonomous Anomaly Classification
SN - 978-989-758-777-1
AU - S. V.
AU - C. S.
AU - M. S.
PY - 2025
SP - 822
EP - 829
DO - 10.5220/0013906400004919
PB - SciTePress