Video Surveillance with Background Subtraction, Filtering, Binarization, and Recognition Technologies

This code implements intelligent video/visual surveillance systems featuring an intuitive human-machine interface. During operation, it captures video streams from computer-connected cameras and employs computer vision techniques including background subtraction (using algorithms like MOG2 or KNN), noise reduction filters (Gaussian/median filtering), image binarization (Otsu's thresholding), and object recognition (contour detection or machine learning models) to monitor targets in real-time.

Developed for versatile surveillance scenarios, the system acquires video input through camera interfaces and processes frames using OpenCV-based background subtraction methods to isolate moving objects, followed by spatial filtering to enhance image quality. Through adaptive thresholding techniques and feature-based recognition algorithms, it achieves precise target monitoring with configurable sensitivity parameters.

The design objective is to provide a streamlined yet effective surveillance solution applicable to residential security, commercial monitoring, and traffic management systems. Users can configure monitoring zones, set detection thresholds, and review alerts through the GUI-based control panel, ensuring flexible system management.

As a robust and scalable surveillance solution, this codebase supports integration with IP cameras and RTSP streams while maintaining cross-platform compatibility. Both individual and commercial users can leverage its modular architecture for customized security applications with real-time notification capabilities.