Smart Surveillance System for Suspicious Activity Detection

Authors: Kiran Murtadak, Rohit Nagargoje, Dnyaneshwar Aher, Prof. A. D. Gawali

Country: India

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Abstract: Crowd management and security are critical challenges in public spaces, where conventional surveillance methods such as CCTV primarily serve as reactive tools for incident tracking rather than proactive crime prevention. Manual monitoring is labour-intensive and inefficient, limiting real-time threat detection capabilities. This paper presents a deep learning-based model for accurate crowd behaviour detection, enabling early identification of suspicious activities to prevent crimes before they occur. The proposed system ensures secure data transmission while maintaining privacy, offering a comprehensive, end-to-end solution for real-time crowd analysis. By leveraging advanced artificial intelligence techniques, this approach enhances public safety, optimizes security operations, and paves the way for smarter surveillance systems.

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Paper Id: 232437

Published On: 2025-04-29

Published In: Volume 13, Issue 2, March-April 2025

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