Depth Gesture Classification Using Extended Forest Vectors
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Solution Overview
Problem
Current depth-based human-computer interaction systems face challenges in accurately and efficiently classifying user gestures under varying circumstances, such as different orientations and user anatomies, using depth data from sensors like Microsoft Kinect and Intel RealSense.
Innovation Solution
The implementation of a processing system that applies plane clipping and classification methods using a forest of trees generated from projected depth images, incorporating features and extended vectors to improve gesture recognition accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification algorithms are used for depth-based gesture recognition, then the system can perform basic object distinction, but the accuracy and speed deteriorate under varying orientations and user anatomies
Solution Approach 1:
The system dynamically adapts classification models based on detected user anatomy and orientation. The classification algorithm adjusts its parameters and decision boundaries in real-time according to the specific user configuration, enabling accurate gesture recognition across diverse orientations and anatomical variations without requiring separate models for each scenario.
Solution Approach 2:
The system changes classification parameters such as feature weights, decision thresholds, and model complexity based on the detected user anatomy and orientation. By adjusting these parameters dynamically, the system maintains high accuracy across varying conditions while optimizing performance for each specific user configuration.
2Measurement precision
If more detailed classification is performed to distinguish finer gesture elements, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing depth data to extract key anatomical landmarks and pose estimates before classification. This preliminary processing identifies the most relevant features and prepares data structures that accelerate the actual classification process, enabling detailed gesture element distinction without proportionally increasing processing time.
Solution Approach 2:
The classification process is segmented into multiple stages: coarse classification to identify major body parts, intermediate classification to distinguish specific limbs and joints, and fine classification to identify individual gesture elements. This hierarchical segmentation allows the system to achieve high measurement precision for fine gesture elements while reducing overall processing time by avoiding unnecessary detailed analysis at all levels.
Data Source
AI summary
Human Computer Interfaces (HCI) may allow a user to interact with a computer via a variety of mechanisms, such as hand, head, and body gestures. Various of the disclosed embodiments allow information captured from a depth camera on an HCI system to be used to recognize such gestures. Particularly, by training a classifier using vectors having both base and extended components, more accurate classification results may be subsequently obtained. The base vector may include a leaf-based assessment of the classification results from a forest for a given depth value candidate pixel. The extended vector may include additional information, such as the leaf-based assessment of the classification results for one or more pixels related to the candidate pixel. Various embodiments employ this improved structure with various optimization methods and structure to provide more efficient in-situ operation.