Frame Difference-based Pedestrian Detection Program with Implementation Details

The frame difference method is a motion detection technique based on video sequences that identifies moving objects by comparing differences between consecutive frames, commonly used in pedestrian detection and surveillance scenarios.

Implementing a frame difference-based pedestrian detection program in MATLAB typically involves the following steps:

Video Reading and Preprocessing The program first needs to read video streams or video files and convert each frame to grayscale images. This step reduces computational load while avoiding interference from color information in motion detection. In MATLAB implementation, this can be achieved using VideoReader() function followed by rgb2gray() conversion for each frame.

Inter-frame Difference Calculation The core of frame difference method lies in calculating differences between adjacent two or three frames. Through simple subtraction operations, motion changes can be extracted. Common approaches include two-frame difference method and three-frame difference method - the former is suitable for simple motion detection, while the latter provides better noise suppression. Code implementation typically uses abs(frame2-frame1) for basic difference calculation.

Binarization and Morphological Processing The difference result is typically a grayscale image containing noise. By setting appropriate thresholds for binarization processing, motion regions can be separated from the background. Subsequently, morphological operations (such as opening and closing operations) remove small noise points and smooth motion region edges. MATLAB's imbinarize() function with adaptive thresholding and imopen()/imclose() functions are commonly employed here.

Target Detection and Pedestrian Identification On extracted motion regions, pedestrians can be located using connected component analysis or contour detection. For complex application scenarios, background modeling or additional features (such as pedestrian shape characteristics or motion trajectories) can be incorporated to improve detection accuracy. The regionprops() function in MATLAB is particularly useful for analyzing connected components and extracting bounding boxes.

Result Visualization Finally, the program can annotate detected pedestrians with rectangular boxes or other markers on original video frames for intuitive observation of detection results. This is typically implemented using insertShape() or rectangle() functions to draw bounding boxes around detected regions.

The frame difference method offers advantages of computational simplicity and good real-time performance, making it suitable for scenarios with stable lighting conditions. However, it's sensitive to lighting changes and susceptible to interference from dynamic backgrounds (such as swaying leaves). Therefore, in practical applications, combining with other algorithms (such as optical flow methods or deep learning models) may be necessary to enhance detection robustness.