Depth Camera Environmental Factor Mitigation
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Solution Overview
Problem
Depth cameras in video game systems face challenges in accurately determining object depth due to environmental factors such as high ambient light and clothing reflectivity, leading to invalid depth information and inaccurate modeling of virtual skeletons.
Innovation Solution
A system that identifies environmental factors causing invalid depth information and outputs modifications to mitigate these factors, using a depth-image analysis method involving test and control images to categorize issues and provide communication or control messages for user intervention or automated adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth cameras use projected infrared light to determine depth, then depth measurement capability is improved, but environmental factors such as ambient light and surface reflectivity cause invalid depth information
Solution Approach 1:
The system performs preliminary classification of environmental factors by comparing test images (captured with infrared illumination) against control images (captured without infrared illumination). This preliminary identification of problematic areas allows the system to categorize the type of environmental interference present before depth calculation, enabling targeted mitigation strategies to be applied subsequently.
Solution Approach 2:
The system establishes a feedback loop where depth image quality is continuously monitored by identifying invalid depth information through image comparison. The classification results feed back into the depth mapping process, allowing the system to adjust its operation based on detected environmental conditions and improve depth measurement accuracy under varying environmental circumstances.
2Measurement precision
If the system captures test and control images to identify environmental factors, then depth information accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the image processing task into distinct functional components: capturing control images without infrared illumination, capturing test images with infrared illumination, comparing the two image sets to identify invalid depth areas, classifying the type of environmental interference, and applying appropriate corrections. This segmentation of the processing pipeline makes the complex system more manageable and allows each component to be optimized independently.
3Reliability
If environmental modifications are applied to mitigate factors, then depth image reliability is improved, but additional processing steps are required
Solution Approach 1:
The system performs self-diagnosis by automatically comparing test and control images to identify environmental interference patterns. It self-corrects by classifying the type of interference and applying appropriate mitigation strategies without requiring external intervention. This self-service capability improves depth image reliability while minimizing the need for additional manual processing steps.
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
The system effectively mitigates environmental factors, improving the accuracy of depth image acquisition and virtual skeleton modeling, ensuring reliable player tracking and game control.
Implementation Method 1
depth cameras use projected infrared light to determine depth of objects in an imaged scene
Data Source
AI summary
A method of depth imaging includes acquiring a depth image from a depth camera, identifying an environmental factor invalidating depth information in one or more portions of the depth image, and outputting an environmental modification to mitigate the environmental factor.