Visual Marker Detection Using Dual-Light IR Confirmation
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Solution Overview
Problem
Detecting visual markers using device-based image processing is challenging, especially with low-resolution images, due to high false alarm rates and resource constraints in devices like toys, mobile phones, and AR headsets.
Innovation Solution
A two-step detection method involving capturing images under different lighting conditions using a standard visual sensor and an IR-capable sensor, with a first detector identifying the visual pattern and a second detector confirming the detection using IR reflection, thereby reducing false positives and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single detector is used to perform image-based detection of a visual marker, then the detection process is simple and fast, but the false alarm rate increases and reliability decreases
Solution Approach 1:
The detection system is divided into two separate detectors: a first detector that performs initial detection on the first image, and a second detector that performs confirmation detection on the second image. This segmentation allows each detector to specialize in specific detection tasks, improving overall reliability while managing complexity through modular design
Solution Approach 2:
A processor acts as an intermediary between the two detectors, receiving detection results from the first detector and coordinating with the second detector to perform confirmation detection. This intermediary manages the complexity of the dual-detector system by centralizing the coordination logic
2Measurement precision
If high-resolution image processing is performed to improve detection accuracy, then measurement precision improves, but resource consumption increases
Solution Approach 1:
The system performs detection on only the necessary portions of images at high resolution. The first detector identifies candidate regions, and the second detector performs confirmation only on those specific regions rather than processing entire high-resolution images, thus reducing resource consumption while maintaining detection precision
Solution Approach 2:
The detection process is segmented into two stages: initial detection on first images and confirmation detection on second images. This segmentation allows the system to process images at appropriate resolution levels for each detection stage, optimizing the balance between precision and resource usage
3Reliability
If image detection is performed under varying lighting conditions, then detection reliability improves, but the complexity of capturing and processing multiple images increases
Solution Approach 1:
The system captures images at different times under different lighting conditions - a first image at a first time and a second image at a second time. This periodic capture approach exploits natural lighting variations to improve detection reliability while using simple temporal scheduling rather than complex active lighting control
Solution Approach 2:
The system uses naturally occurring lighting conditions at different times to provide the variation needed for reliable detection. Rather than requiring complex artificial lighting systems, the approach lets environmental lighting variations serve the detection purpose automatically
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables reliable detection of visual markers in low-resolution images while minimizing resource usage, balancing false alarms and misses, and maintaining user recognition of visual markers under varying lighting conditions.
Implementation Method 1
The visual marker may comprise a first material having a first reflective property in response to the first lighting condition and a second material having a second reflective property in response to the second lighting condition. One of the first and second materials may be an infrared (IR)-reflective material.
Data Source
AI summary
Techniques are presented for detecting a visual marker. A first image containing the visual marker may be captured at a first time under a first lighting condition. A first image-based detection for the visual marker may be performed based on the first image, using a first detector, to produce a first set of results. A second image containing the visual marker may be captured at a second time under a second lighting condition different from the first lighting condition. Based on the first set of results, a second image-based detection for the visual marker may be performed based on the second image, using a second detector different from the first detector, to produce a second set of results.