The "Real-time Home Surveillance System with Face Detection" is a Python project that utilizes computer vision techniques to detect and track faces in a live video stream. The project employs OpenCV, a popular computer vision library, along with the Haar cascade classifier for face detection.
The system continuously captures frames from a webcam and converts them to grayscale for better face detection accuracy. Using the Haar cascade classifier, it identifies faces in the video frames based on predefined patterns and overlays rectangles around them. Additionally, the project timestamps and saves each frame with detected faces as individual image files.
To enhance the project's functionality, it also provides an option to convert the collected frames into a video. By specifying the desired file extension and output filename, the captured frames can be combined into a video file, facilitating easy review and analysis of the surveillance footage.
This Real-time Home Surveillance System with Face Detection project offers a practical solution for monitoring and securing your home environment in real-time, allowing you to keep track of potential intrusions or unauthorized access.