Selective Pixel Array Time-of-Flight Analysis for Safety Monitoring
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Solution Overview
Problem
Existing 3D image sensors require significant processing power and time for depth analysis, making them unsuitable for applications that need fast and reliable response times, such as industrial safety monitoring, where detecting human presence in hazardous areas is critical.
Innovation Solution
An imaging sensor capable of performing selective time-of-flight analysis on specified portions of a pixel array, allowing for 2D analysis on the remaining areas to reduce processing load and improve response time, dynamically selecting areas for 3D analysis based on detected objects and their classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D time-of-flight analysis is performed on the entire pixel array, then depth information and measurement precision are improved, but processing time and power consumption increase significantly
Solution Approach 1:
The pixel array is divided into multiple regions of interest (ROIs), and 3D time-of-flight analysis is performed selectively only on these specific regions rather than the entire array. This segmentation approach maintains depth measurement precision for critical areas while significantly reducing the overall processing time and computational load.
Solution Approach 2:
Different processing qualities are applied to different regions: 3D time-of-flight analysis is applied locally to regions where depth information is critical (such as areas with detected objects or potential hazards), while other regions use standard 2D imaging processing. This local quality differentiation optimizes the balance between measurement precision and processing efficiency.
2Measurement precision
If 3D time-of-flight analysis is performed on the entire pixel array, then depth information and measurement precision are improved, but power consumption increases significantly
Solution Approach 1:
The pixel array is divided into multiple regions of interest (ROIs), and 3D time-of-flight analysis is performed selectively only on these specific regions rather than the entire array. This segmentation approach maintains depth measurement precision for critical areas while significantly reducing the overall processing time and computational load.
Solution Approach 2:
Different processing qualities are applied to different regions: 3D time-of-flight analysis is applied locally to regions where depth information is critical (such as areas with detected objects or potential hazards), while other regions use standard 2D imaging processing. This local quality differentiation optimizes the balance between measurement precision and processing efficiency.
3Loss of time
If selective 3D analysis is performed only on specified portions of the pixel array, then processing time and power consumption are reduced, but depth information coverage is limited
Solution Approach 1:
The system performs preliminary 2D image analysis to detect objects, motion, or other features of interest before performing 3D time-of-flight analysis. This preliminary action identifies which regions require depth information, ensuring that 3D analysis is applied to all areas where it is actually needed rather than arbitrarily selecting regions, thus maintaining comprehensive depth information coverage where required.
Solution Approach 2:
The system uses feedback from 2D image analysis results to dynamically adjust and update the regions of interest for 3D analysis. As objects move or new features are detected, the ROIs are updated accordingly, ensuring continuous and comprehensive depth information coverage for all relevant areas throughout the monitoring period.
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 efficient detection and tracking of human presence with high safety integrity, reducing processing time and power consumption while maintaining accurate distance and trajectory information, making it suitable for fast-response safety applications.
Implementation Method 1
an imaging sensor capable of performing selective time-of-flight analysis on specified portions of a pixel array
Data Source
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AI summary
An imaging sensor device includes pixel array processing functions that allow time-of-flight (TOF) analysis to be performed on selected portions of the pixel array, while two-dimensional imaging analysis is performed on the remaining portions of the array, reducing processing load and response time relative to performing TOF analysis for all pixels of the array. The portion of the pixel array designated for TOF analysis can be pre-defined through configuration of the imaging sensor device. Alternatively, the imaging sensor device can dynamically select the portions of the pixel array on which TOF analysis is to be performed based on object detection and classification by the two-dimensional imaging analysis. Embodiments of the imaging sensor device can also implement a number of safety and redundancy functions to achieve a high degree of safety integrity, making the sensor suitable for use in various types of safety monitoring applications.