IOT CASE STUDY PPT about the surveillance using raspberry pi.pptx
- 1. IoT SECURITY CAMERA WITH RASPBERRY Pi BY, SIDDESH R HARISH MRITHYUNJAYAN S SASIKUMAR M MANOJ KUMAR V
- 2. 1. INTRODUCTION In an era where security concerns are ever-increasing, the integration of emerging technologies offers promising solutions. This project explores the development of a smart security system that combines Internet of Things (IoT) technologies with Artificial Intelligence (AI) for enhanced facial recognition capabilities. Utilizing a Raspberry Pi platform, the system leverages a Passive Infrared (PIR) sensor and a camera module to detect movement and capture images. Through the application of machine learning algorithms, it processes these images to identify individuals, distinguishing between "known" and "unknown" persons. This intelligent system provides real-time, automated security monitoring, reducing the need for manual oversight and improving the overall effectiveness of traditional security methods.
- 3. MODULE DESCRIPTION Motion Detection Module: This module is responsible for detecting movement within the surveillance area using a Passive Infrared (PIR) sensor. When the PIR sensor detects a change in infrared radiation, indicating the presence of a person, it triggers the system to initiate image capture. This ensures that the camera is only activated when there is potential activity, conserving resources and reducing unnecessary processing.
- 4. Image Capturing Module: Upon activation from the motion detection module, this module utilizes the Raspberry Pi Camera Module to capture images of the detected individual. The camera is configured to take high-resolution images quickly to ensure that the captured data is clear and usable for subsequent processing. The module also handles image storage temporarily until the feature extraction process is complete.
- 5. Feature Extraction Module: This module processes the captured images to extract distinguishing features necessary for facial recognition. Utilizing machine learning techniques, it identifies key facial landmarks and characteristics that are critical for accurate identification. The extracted features are then formatted and prepared for comparison against a pre-stored dataset of known individuals.
- 6. Image Recognition Module: The final module is responsible for the actual recognition process. It employs a facial recognition algorithm that compares the extracted features of the captured image against the pre-stored dataset. If a match is found, the individual is flagged as "known," and relevant actions are taken (e.g., logging the incident). If the individual is deemed "unknown," the system logs the information and stores the image for further review. This module enhances the overall security by providing real-time identification and tracking capabilities.
- 7. 2. SYSTEM SPECIFICATION SOFTWARE SPECIFICATION Visual Studio Code PI OS PYTHON WinSCP
- 8. HARDWARE SPECIFICATION Raspberry Pi PIR sensors Pi Camera Module Jumper Cables
- 9. PROPOSED SYSTEM The proposed system is a smart security solution that integrates advanced IoT technologies with AI-powered facial recognition to address the limitations of traditional security systems. Built on a Raspberry Pi platform, the system uses a combination of motion sensors and camera modules to detect movement and capture images only when necessary, conserving resources. When the Passive Infrared (PIR) sensor detects motion, the Raspberry Pi Camera Module is triggered to capture an image of the individual. The captured image is then processed using a machine learning-based facial recognition algorithm that compares the image with a pre-stored dataset of known individuals. The system intelligently categorizes the individual as "known" or "unknown." If the person is recognized as "known," they are flagged accordingly. However, if the person is "unknown," the system logs this information and stores the image for further review.
- 10. The integration of AI allows the system to automate decision-making, providing real-time identification of potential intruders without the need for manual intervention. By analyzing facial features using machine learning techniques, the system ensures that it can accurately identify individuals even in varying conditions. This setup provides a proactive and intelligent approach to security, reducing false alarms and minimizing the need for constant human supervision. Additionally, the system offers scalability, making it adaptable to different environments such as homes, offices, or industrial settings. By combining IoT's real-time data gathering capabilities with AI's analytical power, this proposed system represents a significant improvement over conventional security setups, enhancing both accuracy and efficiency in monitoring and threat detection.
- 12. 5. CONCLUSION The development of this smart security system, integrating IoT technologies with AI-driven facial recognition on a Raspberry Pi platform, demonstrates a significant advancement in modern security solutions. By automating the process of identifying individuals through real- time motion detection and facial recognition, the system addresses key limitations of traditional security methods, such as inefficiencies in manual monitoring and false alarms. With the ability to distinguish between "known" and "unknown" individuals, it provides an intelligent, proactive approach to security, reducing the need for constant human intervention while improving accuracy in identifying potential threats.
- 13. CONCLUSION This project showcases the potential of combining IoT and AI for enhanced surveillance, offering a scalable and efficient solution suitable for various environments. The integration of machine learning techniques allows the system to continuously improve its facial recognition capabilities, ensuring that it can adapt to different lighting conditions, angles, and other variables. Overall, the proposed system is a robust and cost-effective security measure that not only enhances traditional monitoring methods but also offers a future-ready solution for real-time, automated security.