Frame Difference Method for Image and Video Processing

In this document, we explore the application of frame difference method in image and video processing. This technique is particularly suitable for detecting moving objects. The frame difference method is an image processing approach that identifies moving targets by comparing differences between consecutive video frames. The core algorithm typically involves subtracting pixel values of adjacent frames and applying a threshold to generate binary motion masks. Key implementation steps include frame extraction, grayscale conversion, absolute difference calculation, and morphological operations to reduce noise.

This method finds applications across various domains including video surveillance systems, intelligent traffic monitoring, and robotic vision. The frame difference approach provides accurate moving object detection results, thereby enhancing system precision and computational efficiency. From a programming perspective, OpenCV functions like cv2.absdiff() and cv2.threshold() are commonly employed for implementation. Additionally, this technique can be extended to track moving object trajectories and analyze behavioral patterns through multi-frame correlation analysis.

In summary, the frame difference method serves as an effective image processing technique that can be widely implemented in diverse moving object detection scenarios. Its computational simplicity makes it particularly suitable for real-time applications where efficiency is crucial. Common enhancements include background subtraction variants and adaptive thresholding techniques to improve detection accuracy under varying lighting conditions.