Motion Target Detection Using Inter-frame Difference with Multiple Input Images

Inter-frame difference is one of the classical methods for motion target detection, with its core concept involving the identification of moving regions by comparing pixel differences between consecutive frames. This method is computationally efficient and suitable for scenarios requiring high real-time performance.

### Basic Principles When processing multiple consecutive images, changes between adjacent frames are primarily caused by moving objects. By calculating the absolute difference in pixel values between two frames, a difference image can be obtained. Applying threshold processing (such as binarization) to the difference image allows for the separation of moving target regions. When processing multiple frames (like the three-frame difference method), noise interference can be further reduced, improving detection accuracy.

### Implementation Approach Image Preprocessing: Convert input multiple images to grayscale to reduce computational load, and apply operations like Gaussian blur to suppress noise. In code implementation, this typically involves using functions like cv2.cvtColor() for color conversion and cv2.GaussianBlur() for smoothing. Inter-frame Difference Calculation: Sequentially calculate pixel differences between adjacent frames to generate difference images. For example, the difference between frame n and frame n-1 reflects short-term motion changes. Code implementation would use absolute difference operations like cv2.absdiff() between consecutive frames. Threshold Segmentation: Apply thresholds (such as Otsu's method or fixed thresholds) to the difference image to distinguish between background (static parts) and foreground (moving parts). The cv2.threshold() function with appropriate thresholding techniques is commonly used for this step. Post-processing Optimization: Use morphological operations (like dilation and erosion) to eliminate isolated noise points, and perform connected component analysis to merge scattered motion regions. This can be implemented using cv2.morphologyEx() and cv2.connectedComponents() functions.

### Extended Considerations Multi-frame Difference Improvement: Introducing the three-frame difference method (comparing previous, current, and subsequent frames) can better suppress the "ghosting" problem. This requires maintaining a buffer of recent frames and implementing difference calculations between frame(t-1), frame(t), and frame(t+1). Dynamic Background Adaptation: Combining background modeling (like Gaussian Mixture Models) can handle complex scenarios with lighting changes. This involves using algorithms like cv2.createBackgroundSubtractorMOG2() for adaptive background subtraction. Edge Enhancement: Extracting image edge features before difference calculation can improve the completeness of motion target contours. This can be achieved by applying edge detection operators like Canny (cv2.Canny()) prior to difference computation.

This method is suitable for applications such as surveillance and traffic detection, but it is sensitive to rapid global changes (like sudden lighting variations) and requires combination with other algorithms to optimize robustness.