This project is a deep learning framework built for detecting and tracking human body keypoints in images and video streams. It functions as both a real-time motion tracking system and a machine learning environment for training and evaluating pose estimation models.
The system utilizes a two-branch convolutional neural network to predict body part locations and their directional connections simultaneously. It employs multi-stage feature refinement to improve keypoint localization accuracy and uses greedy parsing and bipartite matching algorithms to associate detected parts into individual skeletons. To maintain performance during live video analysis, the framework executes parallel inference across image regions using tensor-based batch processing.
Beyond real-time tracking, the library provides tools for training models on annotated datasets and calculating mean average precision against standardized benchmarks to verify detection quality. The repository includes the necessary components to manage the full lifecycle of pose estimation, from initial model training to performance validation.
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