People Counting with Segmented Infrared Detection
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Solution Overview
Problem
High-resolution imaging sensors for people counting in HVAC systems are costly and consume significant power, while low-resolution sensors fail to achieve accurate counting due to insufficient feature detection, especially when multiple people are in close proximity.
Innovation Solution
A system that combines a pyroelectric infrared sensor with a focal plane array and image processing unit to capture images of a periphery band and an excluded region, detecting relevant motion within the band while ignoring motion outside, and adjusting image capture parameters to reduce power consumption and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging sensors are used for people counting, then counting accuracy is improved, but hardware cost and power consumption increase substantially
Solution Approach 1:
The imaging sensor is divided into two functional regions: a periphery band for detecting motion and entering/exiting objects, and an excluded region for ignoring stationary or irrelevant objects. This segmentation allows the system to use lower resolution sensing while maintaining counting accuracy by focusing computational resources only on relevant areas.
Solution Approach 2:
A pyroelectric infrared sensor acts as an intermediary between the imaging sensor and the counting logic. It detects thermal motion signatures to identify when objects enter or exit the periphery band, triggering counting events without requiring the imaging sensor to continuously operate at high resolution, thereby reducing power consumption while maintaining accuracy.
2Use of energy by moving object
If low-resolution imaging sensors are used, then power consumption and cost are reduced, but counting accuracy deteriorates due to insufficient feature detection
Solution Approach 1:
The system replaces reliance on high-resolution visual feature detection with pyroelectric infrared motion detection. Instead of analyzing detailed visual features that require high-resolution sensors, the system detects thermal motion patterns, which can be accurately captured with lower-resolution sensors combined with motion-sensitive infrared technology.
Solution Approach 2:
The system changes the detection parameter from visual detail resolution to thermal motion sensitivity. By detecting infrared radiation and motion patterns rather than relying on visual feature resolution, the system achieves accurate counting with lower-resolution imaging sensors, as the pyroelectric sensor captures motion events that trigger counting regardless of image resolution.
3Measurement precision
If the imaging sensor continuously captures high-resolution images, then counting accuracy is maintained, but power consumption increases
Solution Approach 1:
Instead of continuous high-resolution imaging, the system uses periodic motion detection via the pyroelectric infrared sensor to trigger imaging only when objects enter or exit the periphery band. This periodic action based on motion events maintains counting accuracy while dramatically reducing the power consumption associated with continuous high-resolution image capture.
4Reliability
If motion detection is performed throughout the entire field of view, then all motion is detected, but relevant motion detection accuracy decreases due to including irrelevant motion in the excluded region
Solution Approach 1:
The field of view is segmented into a periphery band where motion detection is performed and an excluded region where motion is ignored. This segmentation improves relevant motion detection accuracy by focusing detection resources on the periphery band where entering and exiting objects are detected, while excluding irrelevant motion from the excluded region that would otherwise reduce counting accuracy.
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 achieves accurate people counting with reduced power consumption and cost, using a pyroelectric infrared sensor to distinguish people from inanimate objects and an image processing unit to analyze images, thereby enhancing robustness and efficiency.
Implementation Method 1
A periphery band is around an excluded region. For automatically counting physical objects within the periphery band and the excluded region, an imaging sensor captures... Relevant motion is automatically detected within the periphery band... using a pyroelectric infrared sensor to distinguish people from inanimate objects
Data Source
AI summary
A periphery band is around an excluded region. For automatically counting physical objects within the periphery band and the excluded region, an imaging sensor captures: a first image of the periphery band and the excluded region; and a second image of the periphery band and the excluded region. In response to the first image, a first number is counted of physical objects within the periphery band and the excluded region. Relevant motion is automatically detected within the periphery band, while ignoring motion within the excluded region. In response to the second image, a second number is counted of physical objects within the periphery band and the excluded region. In response to determining that a discrepancy exists between the detected relevant motion and the second number, the discrepancy is handled.