Achieving Complete and Reliable Moving Target Images through Background Subtraction

Initially, we can enhance our algorithm by implementing a robust background update model. This involves techniques such as Gaussian Mixture Models (GMM) or running average methods to dynamically maintain an accurate background representation, ensuring precise capture of dynamic changes in video sequences. Subsequently, we employ a novel approach where each frame undergoes subtraction operations with the updated background model. This background subtraction process typically uses functions like cv2.absdiff() in OpenCV, which calculates absolute differences between current frames and the background reference. The resulting difference image undergoes morphological operations (erosion/dilation) and thresholding to extract complete, reliable moving target images. This methodology enables improved understanding and analysis of moving objects in videos, making it highly applicable for research and practical implementations in related fields such as surveillance systems, motion tracking, and video analytics.