Motion Detection Using Three-Frame Difference and Background Subtraction Methods

In this paper, we implement both three-frame difference and background subtraction methods to detect moving objects in image sequences. The three-frame difference method operates by calculating pixel-wise differences between three consecutive video frames (frame_n-1, frame_n, and frame_n+1), typically using absolute difference operations and thresholding to identify moving regions. Meanwhile, the background subtraction approach involves maintaining a dynamic background model (often using Gaussian mixture models or running averages) and comparing current frames against this reference to detect foreground objects. Our implementation includes morphological operations like erosion and dilation to reduce noise in the detection masks. The complete technical documentation provides detailed explanations of the algorithm workflow, parameter optimization strategies, and performance evaluation metrics. Code structure highlights include frame buffer management for temporal operations, adaptive threshold calculation functions, and background model update mechanisms that handle lighting changes effectively.