Abstract:
In today's security-conscious world, the need for advanced surveillance systems is
paramount. This research focuses on pioneering a cutting-edge real-time surveillance
system driven by artificial intelligence (AI) to proactively detect and respond to
potential robbery situations. Utilizing deep learning, this system surpasses traditional
surveillance methods by identifying specific anomalies linked to robberies, such as
individuals wearing helmets and carrying weapons or knives. The system's core
strength lies in its robust object detection and tracking capabilities, powered by state-of-the-art deep learning algorithms analyzing live video streams. It swiftly identifies
anomalies like people entering monitored areas with helmets and various weapons,
such as rifles, handguns, or knives. Upon detection, an automatic alarm is triggered,
notifying security personnel and relevant authorities for immediate action. This study
introduces an innovative automated response feature, allowing security personnel to
take control of the system. Once a robbery is confirmed, it triggers alarm & access
doors can be closed promptly, preventing illegal entry and limiting potential attackers'
movements. This seamless integration of AI-driven detection and automated security
response not only enhances overall security but also reduces human error and
response time, ultimately safeguarding lives and valuable assets. The AI-powered
real-time surveillance system described in this study represents a significant
advancement in security technology. It establishes a new standard for crime control
and prevention due to its ability to adapt to dynamic scenarios, accurately detect
anomalies, and automate security protocols. The research incorporates the latest
object detection frameworks with models for precise object detection and proper
algorithm for efficient object tracking. This technology reshapes the landscape of
modern security systems, offering a robust solution to safeguard public spaces,
corporations, and institutions as threats continue to evolve.
