Gesture Recognition via Point of Interest Trajectory Analysis
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Solution Overview
Problem
Existing gesture-based navigation systems require prior detection and modeling of users or objects, limiting their integration with three-dimensional imaging systems and necessitating background removal and scene calibration.
Innovation Solution
A method involving multi-dimensional representation, constrained clustering, and trajectory analysis for gesture recognition, allowing control of user interfaces without prior detection or tracking, using point of interest candidates with coherent motion to recognize gestures and provide contextual feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior detection and modeling of users is performed, then gesture recognition accuracy is improved, but system complexity and calibration requirements increase
Solution Approach 1:
The patent extracts only the essential motion trajectory information from the scene without performing full user detection or modeling. By taking out only the necessary data (point of interest trajectories) rather than processing complete user models, the system achieves gesture recognition without the complexity of prior detection and calibration systems.
Solution Approach 2:
The system performs self-calibration by automatically adapting to the scene and identifying points of interest without requiring external calibration procedures. The multi-dimensional imaging system autonomously detects and tracks motion patterns, eliminating the need for manual scene calibration while maintaining recognition accuracy.
2Reliability
If background removal and scene calibration are performed, then gesture detection reliability is improved, but processing time and operational complexity increase
Solution Approach 1:
The system performs preliminary identification of points of interest and their trajectories directly from the raw multi-dimensional imaging data without first removing backgrounds or calibrating scenes. By establishing trajectory tracking as the primary action from the outset, the system achieves reliable gesture detection while eliminating time-consuming preprocessing steps.
Solution Approach 2:
The patent replaces mechanical preprocessing operations (background removal, scene calibration) with a computational approach that directly analyzes multi-dimensional imaging data to extract motion trajectories. This substitution eliminates the need for sequential mechanical processing steps while maintaining detection reliability.
3Adaptability or versatility
If multiple users or objects are tracked simultaneously, then system versatility is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the scene into multiple independent point of interest trajectories, each processed separately through the gesture recognition pipeline. By dividing the multi-user/multi-object tracking into discrete trajectory segments, the system achieves high versatility while managing computational complexity through modular, independent processing of each trajectory.
Solution Approach 2:
The system employs a universal gesture recognition framework that processes trajectories from any point of interest regardless of whether it represents a user or object. This multi-functional approach allows the same processing pipeline to handle diverse tracking targets, enhancing versatility without proportionally increasing processing complexity.
Data Source
AI summary
Described herein is a user interface that provides contextual feedback, controls and interface elements on a display screen of an interactive three-dimensional imaging system. A user interacts with the interface to provide control signals in accordance with those recognized by the system to a makes use of at least one POI in a three-dimensional scene that is imaged by the imaging system to provide control signals for the user interface. Control signals are provided by means of gestures which are analyzed in real-time by gesture recognition processes that analyze statistical and geometrical properties of POI motion and trajectories.