Motion Target Detection Program Using Gaussian Mixture Models

In this paper, the author provides a comprehensive introduction to the Gaussian mixture model-based motion target detection program, offering highly valuable insights for implementation. The author explains the fundamental principles of Gaussian mixture models and demonstrates their application in detecting moving targets through practical code implementation. Key algorithm components include background modeling using multiple Gaussian distributions, real-time parameter updates using expectation-maximization techniques, and foreground segmentation through probabilistic thresholding. Additionally, the paper discusses the program's performance metrics, computational efficiency advantages, and limitations under various lighting conditions. The author also suggests optimization strategies such as adaptive learning rates and shadow detection mechanisms for improved accuracy. Overall, this work presents a thorough examination of Gaussian mixture model-based motion detection, serving as an excellent resource for researchers and developers in computer vision applications.