Detection of Criminal Activities through CCTV by Analyzing Live Footage for Mob Formation, Body Language of Suspect

Authors: Mayur Dehade, Anjali Barde, Milind Ghegadmal, Dipak Bhide

Country: India

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Abstract: The growing demand for high security has emphasized the importance of closed-circuit television (CCTV) systems in protecting both public and private spaces. These systems leverage sophisticated algorithms to analyze video feeds from CCTV cameras with the primary objective of detecting and notifying authorities about potential criminal activities. Incorporating machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNN), the system delivers automated surveillance capabilities with a focus on real-time video processing, a monitoring and control interface, and a seamless alerting mechanism integrated with email services. Upon detecting unusual activities, the system immediately generates alerts, sending notifications via email to designated security personnel, law enforcement agencies and property owners. This immediate notification feature facilitates quick response, reduces the increased risk of criminal incidents and increases overall safety in the monitored area.

Keywords: CCN, YOLO, Machine Learning

Paper Id: 230390

Published On: 2023-11-21

Published In: Volume 11, Issue 6, November-December 2023

Cite This: Detection of Criminal Activities through CCTV by Analyzing Live Footage for Mob Formation, Body Language of Suspect - Mayur Dehade, Anjali Barde, Milind Ghegadmal, Dipak Bhide - IJIRMPS Volume 11, Issue 6, November-December 2023.

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