Vehicle Occupancy Detection via Single-Band Infrared Imaging
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Solution Overview
Problem
Current systems for detecting vehicle occupancy in HOV lanes are inefficient and prone to errors due to environmental factors and the need for manual verification, which can be hazardous and disruptive to traffic.
Innovation Solution
A single-band infrared imaging system operating at specific wavelengths (1.4 μm to 2.8 μm) captures images of vehicles to differentiate human skin from other materials, using a threshold reflectance value determined from cumulative histograms to accurately count occupants, enabling automated detection and reducing reliance on human inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visible light is used for automated vehicle occupancy detection, then the system can operate under ideal conditions, but cabin penetration is easily compromised by tinted side windshields and environmental conditions such as rain, snow, and dirt
Solution Approach 1:
The system changes the operational wavelength parameter from visible light to near-infrared illumination (1.4 μm to 2.8 μm). This parameter change enables the system to penetrate tinted windshields and resist environmental interference like rain, snow, and dirt, while maintaining the ability to detect human occupants through their characteristic reflectance patterns in the near-infrared spectrum
2Reliability
If manual enforcement by law enforcement officers is used, then occupancy detection can be performed, but it is difficult and potentially hazardous, disrupting traffic and creating safety risks
Solution Approach 1:
The system replaces the mechanical/manual inspection process with an automated optical detection system using near-infrared imaging. This substitution eliminates the need for law enforcement officers to physically interact with vehicles or occupants, thereby removing the safety hazards and traffic disruption associated with manual enforcement while maintaining detection accuracy through automated image analysis
3Measurement precision
If near infrared illumination is used, then human skin has lower reflectance values making detection more reliable, but the system becomes more complex requiring specific wavelength filtering and processing
Solution Approach 1:
The system applies local quality analysis by examining the reflectance characteristics of specific regions within the vehicle interior. By focusing on the distinctive reflectance pattern of human skin in the near-infrared spectrum (1.4 μm to 2.8 μm), the system can precisely identify occupants despite the increased complexity of the imaging hardware. The local differentiation of reflectance properties enables accurate detection while managing system complexity through targeted spectral analysis
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 provides robust and accurate occupancy detection, resistant to reflectance noise and environmental interference, allowing for automated enforcement of HOV lane regulations and reducing the workload for law enforcement.
Implementation Method 1
a single band infrared (IR) imaging system operating at a pre-defined wavelength range of the electromagnetic spectrum to capture an infrared image of a motor vehicle
Implementation Method 2
human skin, whether light or dark, has reflectance values that are below that of other materials commonly found inside the passenger compartment of a motor vehicle
Data Source
AI summary
What is disclosed is a method for vehicle occupancy detection using a single band infrared imaging system. First, an infrared image of a vehicle intended to be processed for human occupancy detection is captured using a single band infrared camera set to a predefined wavelength band. A candidate sub-image is identified within the captured image. A cumulative histogram is formed using the reflectance values of each pixel in the candidate region. A threshold reflectance value is then determined from the cumulative histogram using a pre-defined cumulative occurrence fraction value which corresponds to a value equivalent to an average sized human face. Embodiments for setting the threshold reflectance value are disclosed. Thereafter, human occupants can be distinguished in the image from the vehicle's interior by comparing pixel reflectances in the sub-image against the threshold reflectance value.