A Crowd Monitoring and Real-Time Tracking System using CNN
Abstract
In response to the ever-evolving landscape of security
concerns, the project endeavors to fortify public safety through
an innovative surveillance system called SECURE SPHERE. The
system strategically places cameras to continuously monitor
crowd behavior, employing cutting-edge algorithms to detect
abnormalities, such as attacking, fighting or the presence of
weapons. Upon identification of anomalies, alerts are promptly
transmitted to law enforcement agencies through an intuitive and
user-friendly application. A distinctive feature of the project lies
in its real-time tracking capability, allowing for the monitoring of
a culprit’s movement captured on any of the strategically
positioned cameras. The proposed innovation significantly
enhances the system’s efficacy in providing precise and
actionable information to law enforcement, thereby bolstering
efforts to ensure public safety. The heart of this initiative lies in
the Application Interface, providing law enforcement with an
accessible and user friendly platform. The interface not only
enables the viewing of alerts but also grants access to the
invaluable real-time tracking feature. The integrated approach to
public safety envisioned in the project ensures that law
enforcement agencies are equipped with the essential tools and
information needed to respond swiftly and effectively to
incidents. By seamlessly combining continuous crowdmonitoring,
anomaly detection, real-time tracking, and user-friendly
interfaces, the project strives to create a safer environment for
the public. The comprehensive surveillance
Keywords:
Crowd Monitoring, Abnormal Activity Detection, Convolutional Neural Network (CNN), Public Safety, Surveillance System, Anomaly Detection, Deep SORT, YOLOPublished
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