Frame Difference Method

In this context, the frame difference method serves as a fundamental approach for moving object detection and segmentation in video sequences. The algorithm works by computing pixel-wise temporal differences between adjacent frames (typically two or three consecutive frames) to identify motion regions. The technical implementation involves two main steps: First, generate a difference image by subtracting corresponding pixel values between frames using operations like cv2.absdiff() in OpenCV. Second, apply binary thresholding (e.g., cv2.threshold()) to this difference image. Under stable lighting conditions, pixels with value changes smaller than a predefined threshold are classified as background, while larger variations indicate moving objects that are marked as foreground pixels. These labeled regions enable precise localization of moving targets within the image. The method's effectiveness stems from the minimal time interval between frames, allowing the previous frame to serve as an efficient background model for the current frame. Notable advantages include: no background accumulation requirements, rapid model updates, straightforward algorithm structure requiring basic arithmetic operations, and minimal computational overhead suitable for real-time systems. The implementation can be optimized using morphological operations (e.g., erosion/dilation) to reduce noise in the detected motion regions.