Pose Recognition Model Channel Fusion for Keypoint Accuracy

The patent segments the key points into five distinct parts (head, torso, limbs, joints, extremities) and processes each part separately through dedicated convolution layers. This segmentation allows the model to capture spatial relationships and dependencies between different body parts, thereby improving recognition accuracy without overwhelming complexity by organizing the processing into manageable functional modules.

The patent introduces a new dimension of analysis by processing key points at multiple levels (individual key point level, local region level, and global body level). This multi-level processing approach adds dimensional depth to the error calculation, enabling the model to capture both local details and global relationships, thus improving accuracy while maintaining structured complexity.

The patent performs preliminary analysis of heat maps by extracting spatial information and calculating relationships between key points before the final error calculation. This preliminary action includes computing distances between key points, analyzing spatial patterns, and preparing feature representations that capture essential spatial relationships, thereby reducing information loss while the efficient convolution operations minimize processing time overhead.

The patent introduces intermediary feature maps and intermediate representations that bridge the raw heat maps and the final error calculation. These intermediaries process and analyze spatial information in a structured manner, acting as mediators that transform raw data into meaningful representations without requiring excessive processing time, thus reducing information loss while maintaining efficiency.

The patent merges the processing of multiple key points and their relationships into a unified multi-level feature extraction framework. By combining individual key point analysis with local and global relationship modeling in a single integrated structure, the model learns interdependencies between key points while avoiding the complexity of separate analysis modules, thus improving reliability without proportionally increasing structural complexity.

The patent creates a universal processing framework that handles multiple tasks simultaneously: extracting individual key point features, calculating spatial relationships, and modeling global body structure. This multi-functional approach allows the model to learn interdependencies between key points for improved performance while maintaining a single versatile architecture rather than multiple specialized models, thus managing structural complexity effectively.