Infrared Moving Target Detection Program: Four MATLAB Implementation Approaches

This program is developed in MATLAB and implements four distinct methods for infrared moving target detection: frame differencing, background subtraction, optical flow analysis, and Gaussian Mixture Modeling (GMM). Each algorithm contains detailed code implementations with explanatory comments, allowing users to understand the technical nuances of infrared target detection. The frame differencing method calculates pixel-wise differences between consecutive frames to identify moving regions. Background subtraction maintains a reference background model and detects foreground objects by comparing current frames against this model. The optical flow method tracks pixel intensity changes between frames to estimate motion vectors. The Gaussian Mixture Model approach uses probabilistic background modeling to handle dynamic scenes with multiple background variations. All code is organized with clear modular structure, featuring key functions like video reading, preprocessing, algorithm core implementation, and result visualization. Beginners can easily modify parameters (such as threshold values, learning rates for GMM, or optical flow parameters) to adapt the detection performance for different scenarios. The implementation demonstrates practical considerations for infrared-specific challenges including thermal noise handling and contrast normalization. This program serves as an excellent educational resource for understanding fundamental computer vision techniques applied to infrared data, providing hands-on experience with algorithm customization and performance optimization for real-world applications.