Posture Prediction Using Action Area and Label Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current posture prediction methods in computer vision are inaccurate in interpreting human actions in three-dimensional scenes.
Innovation Solution
A method that acquires a scene image, identifies an action area, assigns an action label, and uses this information to generate and map a human body posture model into the three-dimensional scene, improving prediction accuracy by combining action area and label data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current posture prediction methods are used, then the process is simple, but the accuracy of posture prediction is poor
Solution Approach 1:
The method segments the posture prediction process into distinct stages: action area identification, action label assignment, posture data acquisition, and posture model mapping. This segmentation allows each component to be optimized independently, improving overall accuracy while maintaining manageable complexity
Solution Approach 2:
The method performs preliminary actions by first identifying action areas and assigning action labels before acquiring posture data. This preliminary classification enables more accurate posture prediction by contextualizing the posture data within identified action regions and their corresponding labels
2Measurement precision
If action area and action label data are combined, then posture prediction accuracy improves, but processing complexity increases
Solution Approach 1:
The method merges action area identification with action label assignment, and combines both with posture data acquisition in an integrated workflow. This merging allows the system to leverage multiple data sources (spatial action areas and semantic action labels) simultaneously to improve posture prediction accuracy without requiring separate independent processing systems
Data Source
AI summary
A posture prediction method includes: acquiring an action area in the scene image by acquiring a scene image obtained by scanning a three-dimensional scene, acquiring an action label corresponding to the action area, the action label is configured to represent the action performed by the human body in the action area, acquiring posture data according to the action area and the action label, acquiring a human body posture model according to the posture data, and mapping the human body posture model into the three-dimensional scene according to the action area and the posture data.