High-Precision Real-Time Video Object Detection and Contour Extraction in Static Backgrounds

In static background environments, we can utilize high-precision video object detection and contour extraction techniques to acquire target information from images in real-time. This technology can be implemented through MATLAB source code programming, which typically involves background subtraction methods like frame differencing or Gaussian Mixture Models (GMM) to isolate moving objects. The implementation process includes image preprocessing, feature extraction, and morphological operations to enhance detection accuracy. Through systematic image processing and analysis, we can precisely detect and extract object contours in video sequences, enabling better understanding and analysis of video data. This approach finds applications in various fields including video surveillance systems, intelligent transportation systems, and computer vision applications. The MATLAB implementation may key functions such as vision.ForegroundDetector for background subtraction, edge detection algorithms like Canny or Sobel operators, and bwboundaries for contour extraction. Therefore, researching and implementing this high-precision video object detection and contour extraction technology holds significant importance and promising application prospects.