Adaptive Cursor Bounding Box for Gesture Tracking
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Solution Overview
Problem
Existing gesture mapping systems in 3D space require calibration and are sensitive to depth jitter, limiting user interaction as they enforce a fixed bounding box that can lead to lost hand tracking when the user moves out of bounds, necessitating larger physical movements and visual feedback to maintain accurate gesture recognition.
Innovation Solution
An adaptive bounding box system that dynamically adjusts its location within the field of view based on the user's hand movement, allowing the box to move with the user's hand when it approaches the edge, ensuring consistent gesture recognition and mapping across varying distances without the need for calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed bounding box is used for gesture mapping, then the system structure is simple, but hand tracking is lost when the user moves out of bounds
Solution Approach 1:
The bounding box is transformed from a static fixed structure to a dynamic adaptive structure that automatically adjusts its position and size based on hand movement. The system continuously updates the bounding box parameters (position, size) in response to detected hand gestures, ensuring the hand remains within bounds while maintaining smooth tracking continuity.
2Measurement precision
If the bounding box size is reduced to improve precision, then measurement precision improves, but the user needs to make larger physical movements to cover the screen
Solution Approach 1:
The system introduces the z-depth dimension to resolve the 2D precision-ease of operation tradeoff. By utilizing depth information, the bounding box can maintain a smaller horizontal footprint for precision while the depth dimension provides additional interaction space, allowing users to perform gestures with natural movement ranges without sacrificing recognition precision.
3Measurement precision
If calibration is performed to improve gesture mapping accuracy, then mapping precision improves, but the system requires user time and setup
Solution Approach 1:
The system performs automatic self-calibration by continuously adapting the bounding box parameters based on detected hand gestures and movement patterns. Instead of requiring explicit user calibration, the system autonomously adjusts mapping parameters in real-time, achieving high mapping accuracy without consuming user time for setup procedures.
4Ease of operation
If the bounding box is made adaptive to follow hand movement, then ease of operation improves, but the system complexity increases
Solution Approach 1:
The system implements a feedback loop where hand position and movement are continuously monitored, and the bounding box parameters are adjusted in response. This closed-loop control provides smooth adaptive interaction while managing complexity through efficient feedback processing and parameter update mechanisms that respond only when necessary.
Data Source
AI summary
Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.