Action Recognition Model Selection Using Principal Components
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Solution Overview
Problem
Existing action recognition systems struggle to accurately classify a plurality of complex actions due to reliance on binary determinations, leading to decreased accuracy in intention estimation.
Innovation Solution
An action recognition system utilizing a server-client configuration with a learning function and action recognition function, employing principal component analysis and machine learning to process body frame information and joint angles, and selecting an action classification model based on principal components to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If binary determination method is used to estimate action intention, then the system is simple to operate, but the accuracy in classifying complex actions of multiple types is markedly decreased
Solution Approach 1:
The patent segments the action recognition process into multiple independent classification models, each specialized for specific action types. Instead of using a single binary determination system, the patent divides the classification task into multiple specialized classifiers that work together to achieve accurate multi-class action recognition while maintaining operational simplicity through modular architecture.
Solution Approach 2:
The patent implements a dynamic selection mechanism that automatically chooses the appropriate classification model based on the input data characteristics. The system dynamically switches between different classification approaches (including binary determination when appropriate and multi-class classification when needed), allowing it to maintain simplicity for straightforward cases while achieving high accuracy for complex actions.
2Measurement precision
If all body frame points are used for action recognition, then the recognition accuracy is improved, but the system becomes vulnerable to occlusion and decreases in reliability
Solution Approach 1:
The patent extracts and utilizes only the necessary body frame points for specific action types rather than requiring all body frame points. By identifying and extracting the minimal sufficient set of features needed for each action classification, the system maintains high recognition accuracy while becoming more robust to occlusion, as missing points do not critically impact performance.
Solution Approach 2:
The patent applies different classification strategies to different body frame regions based on their local characteristics and importance. Each action classification model is trained on specific local features relevant to that action type, allowing the system to maintain high accuracy for each local region while being tolerant of occlusion in other areas.
3Adaptability or versatility
If component analysis is performed to generate statistical components, then the ability to recognize multiple action types is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal component analysis framework that serves multiple classification models simultaneously. The statistical components generated through component analysis are reused across different action classification tasks, allowing the system to recognize multiple action types with a single set of extracted features, thereby reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The patent performs component analysis and generates statistical components in advance, before the actual action classification takes place. This preliminary extraction of features creates a reusable representation that simplifies subsequent classification tasks, as the complex component analysis is performed once and then applied across multiple classification scenarios.
Data Source
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AI summary
An action recognition apparatus performs: a detection process of detecting a shape of an object to be recognized from to-be-analyzed data; a component analysis process of generating one or more components and respective contribution ratios of said components through the component analysis, based on the shape of the object to be recognized; a determination process of determining an ordinal number indicating each dimension of said components based on a cumulative contribution ratio attained from the respective contribution ratios; a selection process of selecting, among the group of action classification models, a specific action classification model in which learning was performed with the same component group as a specific component group including the components with the ordinal number indicating the dimension; and an action recognition process of inputting the specific component group into the specific action classification model, thereby outputting recognition results indicating actions of the object to be recognized.