Depth Sensor Gesture Classification via Plane Clipping
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Solution Overview
Problem
Depth-based human-computer interaction systems face challenges in processing and interpreting dynamic and static environmental data from complex settings, requiring integration of design conditions with mechanical constraints to achieve a successful user experience, especially in environments with varying obstacles and user gestures.
Innovation Solution
The implementation of a depth sensor system that uses preprocessing techniques such as plane clipping and classification methods to isolate and classify depth data, allowing for the inference of user gestures through a processing pipeline that includes pre-processing, classification, and application stages, utilizing features and feature trees to accurately determine user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If depth-based sensors are used to capture environmental data, then the system can remove problems common to optical systems (lighting, shadows, discoloration), but the system introduces complexity in processing and interpreting dynamic and static environmental data
Solution Approach 1:
The patent segments the depth data processing into distinct stages: preprocessing (isolating user from background using plane clipping), feature extraction (identifying gesture-related depth values), and classification (categorizing gestures). This segmentation reduces processing complexity by handling different aspects separately rather than processing all depth data uniformly.
Solution Approach 2:
The patent extracts only the relevant portion of depth data needed for gesture recognition. By using plane clipping to isolate the user from the background and selecting only depth values within the user's spatial bounds, the system removes unnecessary environmental data processing, thereby reducing complexity while maintaining reliability.
2Loss of information
If the system processes all depth data from the environment, then comprehensive environmental understanding is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by establishing the user's spatial bounds and creating clipping planes before processing gesture data. This preprocessing step isolates relevant depth data in advance, so that subsequent gesture recognition only needs to process the already-filtered user portion rather than analyzing the entire environmental dataset, significantly reducing processing time.
Solution Approach 2:
The patent applies different processing quality levels to different portions of depth data. The user region receives detailed processing for gesture recognition, while the background is clipped away and processed minimally. This local quality approach maintains environmental understanding where needed while reducing overall processing time.
3Measurement precision
If plane clipping and classification methods are used to isolate user data, then gesture recognition accuracy improves, but the device complexity increases
Solution Approach 1:
The patent introduces clipping planes as intermediary structures between the raw depth data and the gesture recognition algorithm. These planes act as mediators that automatically separate user from background based on spatial relationships, providing precise gesture isolation without requiring complex machine learning models or additional sensors, thus balancing accuracy with manageable system complexity.
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, the HCI system's depth sensor may capture depth frames of the user's movements over time. To discern gestures from these movements, the system may group portions of the user's anatomy represented by the depth data into classes. “Features” which reflect distinguishing features of the user's anatomy may be used to accomplish this classification. Some embodiments provide improved systems and methods for generating and/or selecting these features. Features prepared by various of the disclosed embodiments may be less susceptible to overfitting training data and may more quickly distinguish portions of the user's anatomy.