This project presents an innovative approach to weapons detection and tracking using a combination of machine learning algorithms, PTZ (Pan-Tilt-Zoom) camera, and a Raspberry Pi4 platform. The system leverages the high-resolution imaging capabilities of PTZ cameras for enhanced surveillance and real-time monitoring. By employing advanced machine learning algorithms, the system is designed to identify and classify weapons with high accuracy. The Raspberry Pi serves as a compact, cost-effective processing unit that integrates with the PTZ camera to facilitate data acquisition and processing. The proposed solution includes a detailed pipeline for image capture, feature extraction, weapon detection, and tracking, ensuring robust performance in diverse environments. The integration of these technologies aims to improve security and safety measures in various settings by providing an intelligent, automated weapon detection and tracking system that is scalable and adaptable. Connect the camera module to the raspberry Pi's camera connector using the ribbon cable provided with the camera. When the camera detects the weapon, then raspberry pi will display the message on LCD and activate the Buzzer for alert. In this project we are using two Servo motors for pan and tilt motion purpose, two DC motors are using for zoom and focus purpose. The 16GB SD card is a key part of the Raspberry Pi; it provides the initial storage for the Operating System and files.

Objectives:

• Develop a Real-Time Weapons Detection System: Utilize machine learning algorithms to accurately identify and classify weapons from video feeds captured by PTZ cameras in real-time, enhancing surveillance capabilities.
• Integrate PTZ Camera Capabilities: Leverage the pan, tilt, and zoom functionalities of PTZ cameras to improve the coverage and detail of video monitoring, ensuring comprehensive detection and tracking of potential threats.
• Optimize Processing on Raspberry Pi: Design and implement a solution that runs efficiently on a Raspberry Pi, providing a cost-effective and compact processing unit capable of handling real-time video analysis and decision-making.
• Enhance Tracking Accuracy: Implement advanced tracking algorithms to follow detected weapons as they move within the camera’s field of view, maintaining continuous and precise monitoring.

Components used:

• Adapter.
• Raspberry pi4.
• SD Card.
• PTZ (Pan-Tilt-Zoom) camera.
• Two Servo motors.
• Two DC motors.
• 16*2 LCD display.
• Buzzer.

Software’s used:

1. Raspbian OS.
2. Python language.
3. Artificial intelligence and Machine learning.
4. Express SCH for Circuit design.

   

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