Frame Difference Algorithm Implementation for AVI Video Motion Detection

In this project, we need to read an AVI video file and perform frame difference algorithm operations to detect moving objects. Specifically, the frame difference method works by comparing pixel values between consecutive video frames to identify moving targets. When the absolute difference between corresponding pixels exceeds a predefined threshold, those pixels are classified as belonging to moving objects and marked in the video output. The implementation typically involves the following steps: First, we use video reading functions (like VideoReader in MATLAB or OpenCV's VideoCapture) to load the AVI file frame by frame. Then, we convert color frames to grayscale for efficiency. The core algorithm calculates the absolute difference between current and previous frames using functions such as absdiff(). A threshold operation (using methods like threshold() or simple comparison operators) is applied to create a binary mask where high-value pixels indicate motion regions. Morphological operations (erosion and dilation) may be used to reduce noise and fill gaps in detected regions. Finally, contour detection or bounding box functions can mark the moving objects' positions and sizes in the original video. This approach requires substantial computational processing as it involves frame-by-frame analysis, but it effectively detects moving targets in video sequences with relatively simple implementation and real-time capability for moderate-resolution videos.