Real Time AI Enhanced Crowd Surveillance with Big Data Analytics

Authors: Prof. M.S. Khan, Bhalerao Chaitrali, Bangar Gaurav, Kharat Sakshi, Pingale Yukta

Country: India

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Abstract: The surveillance system employs state-of-the-art AI algorithms to process live video feeds, extracting meaningful insights from the complex dynamics of crowds. Furthermore, the incorporation of big data analytics facilitates the storage, manage ment, and rapid analysis of vast amounts of surveillance data. This not only enhances the real-time monitoring capabilities but also enables historical trend analysis for pre dictive modeling. Key components of the proposed system include a sophisticated camera network, edge computing capabilities for immediate processing of video data, and a centralized big data infrastructure. The AI models are trained to recognize abnormal crowd behavior, such as sudden movements, overcrowding, or potential se curity incidents. The system’s real-time alerts empower security personnel to respond swiftly to emerging situations, thus improving overall public safety. The proposed real-time AI-enhanced crowd surveillance system with big data analytics represents a holistic approach to urban security, leveraging cutting-edge technologies to enhance situational awareness and response capabilities. By amalgamating the strengths of AI and big data, this system stands at the forefront of intelligent crowd monitoring, contributing to the creation of safer and more secure urban environments.

Keywords: Artificial Intelligence, surveillance system, real-time alerts, big data

Paper Id: 230623

Published On: 2024-05-03

Published In: Volume 12, Issue 3, May-June 2024

Cite This: Real Time AI Enhanced Crowd Surveillance with Big Data Analytics - Prof. M.S. Khan, Bhalerao Chaitrali, Bangar Gaurav, Kharat Sakshi, Pingale Yukta - IJIRMPS Volume 12, Issue 3, May-June 2024.

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