Target Tracking Simulation Program

Target tracking represents one of the critical applications in computer vision. This simulation program implements a straightforward yet effective target tracking solution using MATLAB to process AVI-format videos. The system specifically focuses on contour extraction and movement direction analysis for human subjects against black backgrounds. Below is the implementation approach with code-related details. The video input module begins by loading AVI video files using MATLAB's VideoReader function. Given the black background, the program employs background subtraction methodology. This technique compares current frames against a background model (often created by averaging initial frames) to rapidly detect moving human contours through pixel-wise differential operations. Next, the binary foreground images undergo optimization through threshold processing and morphological operations. The imbinarize function with adaptive thresholding eliminates noise, while morphological operations like erosion (imerode) and dilation (imdilate) fill contour cavities and refine shape boundaries. Edge detection algorithms such as Sobel (edge function with 'sobel' option) or Canny operators further enhance contour definition by highlighting significant gradients. The program then calculates either the centroid (using regionprops for centroid detection) or bounding boxes of human contours. Movement direction analysis employs optical flow (via opticalFlowHS or opticalFlowFarneback functions) or frame difference methods. By tracking position changes across consecutive frames and calculating displacement vectors, the system estimates target velocity and movement trends through simple kinematic equations. Finally, simulation results are visualized through MATLAB's plotting capabilities, displaying original video frames, extracted contours, and annotated movement directions using quiver plots for vector visualization. This program suits basic motion analysis scenarios like pedestrian monitoring and action recognition, with potential extensions for multi-target tracking (using assignment algorithms like Hungarian method) and complex background handling through Gaussian Mixture Models (vision.ForegroundDetector).