Image Sensor Occupancy Detection Using Aggregated Zone Maps
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Solution Overview
Problem
Existing lighting fixtures with integrated image sensors face challenges in deriving useful environmental information efficiently and reliably due to limited processing power, leading to increased cost and complexity.
Innovation Solution
A method involving downscaled zone maps from high-resolution image frames, combined with neural networks, to determine environmental conditions such as occupancy, using zone value aggregation, threshold comparisons, and verification processes to minimize processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an image sensor is integrated into a lighting fixture to increase monitoring resolution, then the resolution at which the lighting fixture is capable of monitoring the surrounding environment is improved, but the processing power required to derive useful information from the large amount of data increases significantly
Solution Approach 1:
The image frame is divided into multiple zones, and pixel values within each zone are aggregated to produce a single zone value. This segmentation approach reduces the total number of data points from thousands of individual pixels to a manageable number of zone values, enabling processing on devices with limited power while maintaining monitoring capability.
Solution Approach 2:
The patent extracts only the essential information needed for environmental monitoring by aggregating pixel values into zone values. Instead of processing all raw pixel data, the system extracts meaningful zone-level information that suffices for determining occupancy and ambient light levels, thereby reducing processing requirements.
2Reliability
If conventional sensors are used alongside an image sensor to ensure reliable environmental condition detection, then the reliability of environmental condition detection is improved, but the cost and complexity of the device increase
Solution Approach 1:
The image sensor is made multi-functional by using it for both high-resolution monitoring and environmental condition detection. Through zone value aggregation and neural network processing, the same sensor serves multiple purposes, eliminating the need for separate conventional sensors and reducing overall device complexity while maintaining reliability.
Solution Approach 2:
The patent creates a simplified representation (zone maps) that copies only the essential features of the full-resolution image data. This copied data structure maintains the necessary information for environmental detection while being computationally efficient to process, achieving reliable detection without additional hardware.
3Measurement precision
If sophisticated image analysis methods are used to accurately determine environmental conditions, then the accuracy of environmental condition determination is improved, but the computational resources required increase beyond what is available in lighting fixtures
Solution Approach 1:
By segmenting the image into zones and aggregating pixel values, the patent creates a compressed representation that retains essential information for environmental detection. This segmentation enables accurate determination of occupancy and light levels while reducing computational complexity to levels suitable for lighting fixture processors.
Solution Approach 2:
The patent transforms the data representation from individual pixel values to aggregated zone values, changing the parameter scale and resolution. This parameter transformation maintains the necessary information for accurate environmental condition determination while making the data suitable for processing with limited computational resources.
Data Source
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AI summary
A method includes the steps of obtaining a frame from an image sensor, the frame comprising a number of pixel values, detecting a change in a first subset of the pixel values, detecting a change in the second subset of the pixel values near the first subset of the pixel values, and determining an occupancy state based on a relationship between the change in the first subset of the pixel values and the second subset of the pixel values. The occupancy state may be determined to be occupied when the change in the first subset of the pixel values is in a first direction and the change in the second subset of the pixel values is in a second direction opposite the first direction.