Hybrid Human Body Recognition Using Segmented Learning and Modeling

The patent segments the human body into multiple regions (head, torso, limbs) and processes each region separately using the learning-based recognition unit. This segmentation allows the system to focus computational resources on specific body parts rather than processing the entire image globally, reducing overall data storage and calculation requirements while maintaining high recognition precision for each body part.

The patent introduces a spatial dimension by dividing the image into multiple regions of interest and processing them in parallel. The learning-based recognition unit operates on segmented regional data rather than the complete image, effectively transforming a single high-dimensional processing task into multiple lower-dimensional tasks, thereby reducing data storage and computational complexity.

The patent divides the complex task of full-body recognition into simpler sub-tasks by segmenting the image into multiple body regions. Each region is processed independently by the learning-based recognition unit with simpler calculation requirements, reducing overall computational complexity while maintaining high precision through focused regional analysis.

The patent applies partial action by using the learning-based recognition unit only for specific body regions where high precision is most critical, rather than applying it uniformly across the entire image. This selective application reduces calculation complexity while maintaining recognition precision where it matters most.

The patent merges two previously separate recognition systems into a hybrid architecture. The learning-based recognition unit provides high-precision detection for body regions, while the modeling-based recognition unit handles movement tracing and temporal consistency. This combination allows the system to achieve both high precision (from learning-based) and high speed (from modeling-based) simultaneously.

The controller acts as an intermediary that coordinates between the learning-based recognition unit and the modeling-based recognition unit. It integrates the high-precision regional detection results with the high-speed movement tracing capabilities, mediating between the two systems to achieve both precision and speed in the overall recognition process.