This project leverages deep learning techniques to create an intelligent CCTV system capable of detecting and identifying objects and motion in real-time. By integrating object detection with face recognition and motion tracking, the system can monitor entry/exit activity, identify visitors, and track suspicious behaviors or stolen items. This solution can be used in various security applications, from home surveillance to larger security setups, providing an automated, efficient way to monitor live CCTV feeds.
- Object and Motion Detection: Identifies objects and tracks movement within the CCTV footage.
- Face Recognition: Uses Haar Cascade classifiers to recognize faces in real-time.
- Automated Entry/Exit Monitoring: Tracks people entering and exiting the monitored area.
- Customizable Alerts: Detects specified objects or activities and generates alerts for real-time response.
- Data Logging and Recording: Logs activity and provides options for recording footage for future reference.
git clone https://github.com/Chowdhurynaseeh/Live_CCTV_Using_Deep_Learning_for_object_detection.git
cd Live_CCTV_Using_Deep_Learning_for_object_detection
# Install Dependencies
Install the required packages listed in requirements.txt (if available) or ensure dependencies like OpenCV, TensorFlow/PyTorch, and relevant deep learning libraries are installed.