Handheld Object Target Location Detection Using Calibration Factors
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Solution Overview
Problem
Existing systems face challenges in accurately determining the projected target location of a handheld object due to factors like eye dominance, head tilting, and arm movement, leading to discrepancies between user perception and camera detection.
Innovation Solution
The system uses a camera to detect a reference element on the handheld object, applying translation factors and scaling factors to compensate for these discrepancies, and employs polynomial regression to account for arcuate arm movements, allowing for a single-point calibration to determine accurate projected target locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a camera detects the location of a reference element on a handheld object to determine target location, then the system can automatically detect pointing direction, but physical characteristics of the user's body (eye dominance, head tilting, arm movement) cause discrepancies between user perception and camera detection
Solution Approach 1:
The system performs a calibration process before normal operation where the user points the handheld object at a known calibration location. The system captures an image during this calibration and uses it to determine translation factors and scaling factors that will be applied to subsequent target location detections. This preliminary action establishes a reference frame that accounts for individual user characteristics, thereby resolving the discrepancy between automated detection and user perception.
Solution Approach 2:
The system changes parameters by applying translation factors and scaling factors to the detected reference element location. These factors are derived from the calibration image and are used to transform the raw camera coordinates into accurate target location coordinates. This parameter transformation compensates for the effects of eye dominance, head tilting, and arm movement, maintaining measurement precision while preserving automated detection.
2Measurement precision
If the system applies translation factors and scaling factors to compensate for body characteristics, then accuracy of target location is improved, but calibration process becomes more complex
Solution Approach 1:
The calibration process is performed once before normal operation, capturing a calibration image at a known location. This preliminary action establishes all necessary translation and scaling factors in advance, so that during normal operation, the system simply applies these pre-computed factors without additional complexity. The one-time calibration burden is acceptable because it enables accurate automated detection for all subsequent operations.
Solution Approach 2:
The system uses the user's own calibration image to generate the translation and scaling factors specific to that user's body characteristics. The calibration process is self-contained, requiring only that the user point the handheld object at the calibration location, after which the system automatically computes and stores the necessary correction factors without requiring manual intervention or complex setup procedures.
3Measurement precision
If polynomial regression is used to account for arcuate arm movements, then the system compensates for non-linear motion patterns, but computational complexity increases
Solution Approach 1:
The polynomial regression model is pre-computed during the calibration process based on the calibration image and known arm movement characteristics. The regression coefficients are stored and then simply applied during normal operation through straightforward polynomial evaluation. This transforms a potentially complex real-time computation into a simple application of pre-computed parameters, maintaining precision while reducing operational complexity.
Data Source
AI summary
A projected target location of a handheld object is determined based on applying translation factors, scaling factors, and offsets to a location of a reference element of the handheld object detected by a camera on a two-dimensional plane. The translation factors are determined based on a difference between a calibration location on the plane and an initial location of the reference element corresponding to the calibration location, and serve to shift the location of the reference element to generate the projected target location. The scaling factors are determined based on an estimated length of a user's arm holding the handheld object, and serve to scale the location of the reference element to generate the projected target location. The offsets are determined based on polynomial equations, and serve to extend the distance between the projected target location and the calibration location.