Frame Difference Method for Video Image Acquisition and Moving Object Detection

The frame difference method is a technique for video image acquisition and moving object detection. By comparing differences between consecutive video frames, it identifies moving objects in the image sequence. This pixel-level comparison method enables fast and accurate motion detection in real-time applications. To implement the frame difference method, the process typically involves: 1. Reading video frames using functions like VideoReader() in MATLAB or cv2.VideoCapture() in OpenCV 2. Converting frames to grayscale using RGB-to-gray conversion algorithms (e.g., cv2.cvtColor() with COLOR_BGR2GRAY flag) 3. Calculating the absolute difference between consecutive frames using functions such as absdiff() 4. Applying thresholding operations (e.g., cv2.threshold() with binary thresholding) to create a binary mask of moving regions 5. Performing morphological operations (like erosion/dilation) to reduce noise and enhance object detection 6. Contour detection and bounding box drawing to mark identified moving objects for further analysis The key algorithm involves computing: Difference_frame = |Frame(t) - Frame(t-1)| After thresholding, connected component analysis helps identify coherent moving objects. This method is particularly effective for static camera scenarios with minimal background changes.