Color Calibration for Object Tracking in Gesture Systems
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Solution Overview
Problem
Conventional gaming systems lack effective color calibration methods for tracking objects, leading to inaccurate object tracking due to environmental factors and color variations, which affects the reliability of gesture-based input systems.
Innovation Solution
A method for calibrating a tracking system by determining trackable colors using a computing device that causes an object to assume predefined colors, identifies pixels corresponding to these colors, and separates object colors from environment colors to determine usable colors for accurate tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tracking systems are used without color calibration, then the system operation is simple, but the object tracking accuracy deteriorates due to environmental factors and color variations
Solution Approach 1:
The system performs color calibration before actual object tracking begins. The processor determines trackable colors by analyzing environment colors and object colors in advance, establishing a calibration lookup table that maps object colors to trackable colors. This preliminary calibration action ensures accurate tracking without requiring complex continuous adjustments during operation.
Solution Approach 2:
The calibration lookup table serves as an intermediary data structure between the raw color data from the image sensor and the trackable color information needed for object identification. This intermediary component translates environment-affected color measurements into corrected object color representations, enabling accurate tracking while maintaining simple system architecture.
2Reliability
If color calibration is performed to distinguish object colors from environment colors, then object tracking reliability improves, but the calibration process time increases
Solution Approach 1:
The system performs partial color calibration by sampling a limited set of predefined colors (trackable colors) rather than analyzing the entire color spectrum. The processor identifies environment colors and object colors at specific color points, creating a manageable calibration lookup table that provides sufficient reliability for gesture recognition without requiring exhaustive color analysis.
Solution Approach 2:
The system creates a simplified representation (copy) of the color calibration data in the form of a lookup table that maps object colors to trackable colors. This copied calibration information can be quickly referenced during gesture recognition without reperforming the complex color analysis, reducing the time cost of repeated calibration operations.
3Measurement precision
If the object is caused to assume predefined colors for calibration, then color identification accuracy improves, but the object operation complexity increases
Solution Approach 1:
The system changes the color parameter of the tracked object to a set of predefined trackable colors during calibration. The processor controls the object to assume these specific colors (such as red, green, blue, yellow, cyan, magenta) which are easily distinguishable from environment colors. This parameter change approach simplifies color identification while maintaining ease of operation through straightforward color switching.
Data Source
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AI summary
To calibrate a tracking system a computing device locates an object in one or more images taken by an optical sensor. The computing device determines environment colors included in the image, the environment colors being colors in the one or more images that are not emitted by the object. The computing device determines one or more trackable colors that, if assumed by the object, will enable the computing device to track the object.