Transform traditional surveillance systems with the power of Deep Learning to enhance security and safety. This Smart CCTV system incorporates real-time object detection, facial recognition, anomaly detection, automated alerts, and advanced analytics.
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Real-time Object Detection:
- Utilizes state-of-the-art Deep Learning models for accurate and efficient object detection.
- Provides real-time analysis of video feeds to identify and track objects.
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Facial Recognition:
- Implements facial recognition technology for identifying individuals in the surveillance footage.
- Enhances security by allowing for the identification of known individuals.
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Anomaly Detection:
- Detects unusual patterns or behaviors in the monitored area.
- Raises alerts for potential security threats or abnormal activities.
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Automated Alerts:
- Sends automated alerts in real-time when suspicious activities or objects are detected.
- Improves responsiveness by notifying security personnel promptly.
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Advanced Analytics:
- Generates comprehensive analytics reports for historical data analysis.
- Provides insights into patterns, trends, and potential security risks.
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Motion Prediction Improvement:
- Achieved an 86.7% accuracy improvement in motion prediction using the Haar Cascade algorithm.
- Enhances the system's ability to predict and track moving objects with higher precision.
- Programming Language: Python
- Web Framework: Django
- Computer Vision Library: OpenCV
- Object Detection Algorithm: Haar Cascade
- Databases: MongoDB, MySQL
- Video Streaming Protocol: RTSP
- Email Notification: SMTP
- Web Technologies: HTML, CSS, Javascript
- Cloud Platform: Azure
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Clone the repository:
https://github.com/SHAMSUNDAR20/Smart-CCTV-Using-Deep-Learning.git cd Smart-CCTV-Using-Deep-Learning