Motion Object Detection using Gaussian Mixture Models with Comprehensive Clustering Toolkit

This toolkit package includes comprehensive clustering toolboxes, featuring complete source code for motion object detection using Gaussian Mixture Models (GMM). The GMM implementation employs expectation-maximization algorithms for parameter estimation and Bayesian inference for foreground-background classification. Additionally, the package contains various other algorithms and techniques applicable for object recognition and tracking tasks. Within this toolkit, you can find implementations of diverse clustering algorithms tailored for different application scenarios, including density-based clustering (DBSCAN with epsilon-neighborhood queries), hierarchical clustering (agglomerative methods with linkage variations), and spectral clustering (graph Laplacian-based approaches). The package also provides essential utility functions and preprocessing modules featuring data normalization routines, distance metric computations, and dimensionality reduction techniques to facilitate more efficient and streamlined clustering workflows. Whether you're working in computer vision (with OpenCV integration capabilities), machine learning (compatible with scikit-learn pipelines), or data analytics domains, this toolkit offers extensive resources and tools to support various clustering tasks through well-documented APIs and modular code architecture.