Paper Details
Identification of Criminal Behavior via CCTV by Monitoring Real-Time Footage for Crowd Formation and Suspect Body Language
Authors
Prof. Salunke A. A, Prof. Ansari K. R, Prof. Jape N. V, Prof. Ansari A. M
Abstract
The increasing need for enhanced security has highlighted the critical role of closed-circuit television (CCTV) systems in safeguarding both public and private spaces. These systems utilize advanced algorithms to process video streams from CCTV cameras, aiming to identify and alert authorities about possible criminal activities. By integrating machine learning and deep learning approaches, particularly Convolutional Neural Networks (CNN), the system offers automated surveillance capabilities that focus on real-time video analysis, featuring a user-friendly monitoring and control interface along with a robust alert mechanism linked to email services.When the system detects suspicious activities, it instantly triggers alerts, dispatching notifications via email to assigned security personnel, law enforcement, and property owners. This real-time notification system enables swift responses, mitigating potential risks of criminal incidents and enhancing overall safety in the monitored environment.
Keywords
CCN, YOLO, Machine Learning, Criminal Activity, Safety, CCTV etc.
Citation
Identification of Criminal Behavior via CCTV by Monitoring Real-Time Footage for Crowd Formation and Suspect Body Language. Prof. Salunke A. A, Prof. Ansari K. R, Prof. Jape N. V, Prof. Ansari A. M. 2024. IJIRCT, Volume 10, Issue 5. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2410046