Explore MATLAB source code curated for "视频监控" with clean implementations, documentation, and examples.
A MATLAB-based video surveillance system that triggers voice alerts when objects intrude the monitoring area. Current implementation requires velocity threshold adjustments for fast-moving objects. Potential enhancements include integrating audio analysis for sound fluctuation detection and voice recognition capabilities. The system utilizes background subtraction algorithms and real-time processing techniques for motion detection.
Robot Vision Toolbox with extensive routine libraries, useful for video surveillance, object detection and tracking applications. Includes implementation of computer vision algorithms and pre-built processing pipelines.
Implementation of multi-target tracking using the Multi-Hypothesis Testing method, featuring a video surveillance platform integrated with Kalman filtering for target trajectory prediction. Users can customize the codebase according to their specific requirements.
MATLAB video surveillance program that captures an image every 10 seconds using timer-based frame acquisition.
This code implements shadow removal for intelligent visual monitoring/video surveillance systems. Shadow elimination significantly enhances target detection by reducing false positives and missed detections, improving recognition efficiency, and increasing system stability and reliability. The implementation typically involves background modeling, color/texture analysis, and morphological operations to distinguish shadows from foreground objects.
Multi-object detection technique applicable to video surveillance, visual object tracking, motion detection, and other related image processing applications. Includes implementation approaches using detection algorithms and computer vision toolbox functions.
A MATLAB-based application for video surveillance with computer vision capabilities
Custom-developed video surveillance experiment program for vehicle passage detection, complete with experimental data sets and implementation details.
This implementation constructs a Gaussian Mixture Model (GMM) designed for computer vision applications including video object detection, video surveillance, motion detection, moving object detection, and video object tracking. The code features parameter optimization and expectation-maximization algorithm implementation for robust multi-modal data modeling.
This code implements intelligent video/visual surveillance with an intuitive user interface. It captures live video streams from computer-connected cameras and processes images using background subtraction, filtering, binarization, and recognition techniques to monitor targets, with OpenCV integration for real-time processing capabilities.