Frame Difference Method Source Code

The frame difference method discussed here is one of the most widely used approaches for moving object detection and segmentation. The core algorithm operates by performing pixel-level temporal differencing between consecutive frames (typically two or three frames) in an image sequence, followed by binarization to extract moving regions. The implementation generally follows these steps: First, subtract corresponding pixel values between adjacent frames to generate a difference image. Second, apply binary thresholding to the difference image. Under conditions with minimal environmental brightness variations, pixels showing changes smaller than a predetermined threshold are classified as background pixels. Conversely, regions with substantial pixel value changes are identified as being caused by moving objects and marked as foreground pixels. These labeled pixel regions enable precise localization of moving targets within the image. This method is particularly valuable in computer vision applications for real-time motion detection and video surveillance systems. From a coding perspective, the implementation typically involves: - Reading consecutive frames using video processing libraries (e.g., OpenCV's VideoCapture) - Converting frames to grayscale for efficient computation - Calculating absolute differences between frames using functions like cv2.absdiff() - Applying thresholding operations (cv2.threshold()) with optimal threshold values - Performing morphological operations to reduce noise and enhance object detection - Contour detection to identify and locate moving objects precisely The method's efficiency makes it suitable for real-time applications, though it may struggle with slow-moving objects or sudden lighting changes without additional preprocessing techniques.