IR Emitter Array Lookup Table for Depth Determination
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Solution Overview
Problem
Current video surveillance systems face challenges in efficiently utilizing infrared emitters to provide accurate depth information and reduce false security alerts, often resulting in unnecessary data collection and increased user complexity.
Innovation Solution
The implementation of a process that generates lookup tables using a camera system's IR illuminators and image sensors to estimate spatial depth, classify objects, and create depth maps, allowing for more precise identification of scene properties and camera positioning, while also reducing false alerts by distinguishing between relevant and irrelevant motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrared emitters are used to illuminate the scene for depth determination, then depth information can be obtained, but the system complexity and false alert rates increase due to unnecessary data collection
Solution Approach 1:
The image sensor array is divided into multiple regions of interest (ROIs), each associated with specific infrared emitters. The system selectively activates only the emitters corresponding to active ROIs, segmenting the illumination task to reduce unnecessary data collection and system complexity while maintaining depth measurement capability in relevant areas.
Solution Approach 2:
Instead of activating all infrared emitters across the entire sensor array, the system applies partial action by enabling only the subset of emitters that correspond to currently monitored regions of interest. This reduces the overall system complexity and false alert rates while still providing sufficient depth information for security monitoring purposes.
2Reliability
If all infrared emitters are activated to ensure complete scene coverage, then comprehensive depth data is collected, but false security alerts increase and user complexity rises
Solution Approach 1:
Different regions of the sensor array are assigned different qualities of illumination based on their security importance. High-priority regions receive full infrared illumination for reliable depth data, while low-priority regions use reduced or no illumination. This local differentiation maintains scene coverage completeness for critical areas while reducing user complexity and false alerts overall.
Solution Approach 2:
The system performs preliminary identification of regions of interest before activating infrared emitters. By pre-determining which areas require monitoring based on motion detection or other triggers, the system activates only the necessary emitters in advance, ensuring complete coverage of important areas while minimizing unnecessary illumination and associated complexity.
3Reliability
If continuous monitoring of the entire scene is performed, then no motion events are missed, but data processing load and false alerts increase
Solution Approach 1:
The monitoring scene is segmented into multiple regions of interest, each with its own motion detection parameters. The system processes motion data separately for each ROI rather than analyzing the entire scene uniformly. This segmentation maintains reliable motion detection completeness within each region while significantly improving overall data processing efficiency by reducing the total data volume that requires analysis.
Solution Approach 2:
The system applies partial monitoring action by focusing computational resources only on regions where motion has been detected or where security events are most likely to occur. Instead of continuously processing data from the entire scene, the system dynamically adjusts monitoring intensity based on activity levels in different areas, maintaining detection completeness for critical events while improving productivity through selective processing.
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 enhances the efficiency and accuracy of video surveillance by providing detailed depth information, improving object classification, and minimizing false alerts, thereby simplifying user interaction and data management.
Implementation Method 1
a plurality of IR illuminators in fixed locations relative to the array of image sensors
Implementation Method 2
determining an expected IR light intensity at the respective pixel based on the respective depth
Data Source
AI summary
A process generates a lookup table to estimate spatial depth in a visual scene. The process identifies subsets of illuminators of a camera system with image sensors and illuminators. The image sensors are associated with multiple pixels. For each pixel, and for each of multiple depths from the pixel, the process simulates a virtual surface at the depth. For each subset of the subsets of illuminators, the process simulates illumination of the virtual surface from the subset and determines an expected light intensity at the pixel from light reflected from the virtual surface due to the simulated illumination. The process forms intensity information from the expected light intensity determined for the pixel for each of the depths and each of the subsets. The process constructs a lookup table comprising the intensity information. The lookup table associates the intensity information for each pixel with the respective depth and the respective subset.