Seat Occupancy Detection Using Depth Height Profiles
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Solution Overview
Problem
Existing methods for detecting seat occupancy in vehicles are inadequate, particularly in terms of accuracy and robustness, especially when dealing with varying passenger configurations and temporary objects.
Innovation Solution
A method using an imaging device to capture depth data images, determine height profiles along multiple lines, and compare them with reference profiles to detect seat occupancy, employing machine learning algorithms for enhanced accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging methods are used to detect seat occupancy, then the system is simple to implement, but the detection accuracy is insufficient especially with varying passenger configurations and temporary objects
Solution Approach 1:
The patent divides the detection process into multiple segments: capturing multiple images at different timestamps, determining height profiles along multiple lines in each image, comparing each profile with reference profiles, and using machine learning algorithms to classify occupancy. This segmentation allows each component to be optimized independently, improving overall detection accuracy while managing system complexity through modular processing steps.
Solution Approach 2:
The patent transitions from traditional 2D image analysis to 3D depth-aware analysis by determining height profiles along multiple lines and comparing them with reference profiles. This adds a depth dimension to the detection process, enabling the system to distinguish between actual occupants and temporary objects based on their vertical position and height characteristics, thereby improving detection accuracy.
2Measurement precision
If multiple height profiles and machine learning algorithms are used, then detection accuracy and spatial-temporal resolution improve, but processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images at different timestamps before final classification, and by pre-determining reference height profiles from unoccupied seats. These preliminary steps create a temporal baseline and reference framework that simplifies the subsequent machine learning classification process, allowing the system to detect changes more efficiently with reduced computational complexity during real-time operation.
Solution Approach 2:
The patent creates multiple copies of height profiles along different lines and at different timestamps, and generates reference profiles that are copied and compared against actual measurements. This copying approach allows the machine learning algorithm to work with replicated data structures, improving statistical analysis and detection accuracy while maintaining manageable processing complexity through efficient data reuse.
3Reliability
If reference profiles from unoccupied seats are used for comparison, then false positives are reduced, but the system requires additional calibration time and data collection
Solution Approach 1:
The patent performs preliminary data collection and reference profile generation during vehicle setup or initial operation periods. By capturing images when seats are known to be unoccupied and generating reference height profiles in advance, the system establishes a baseline for comparison before actual occupancy detection begins. This preliminary action reduces false positives by having accurate reference data ready, while the calibration time is concentrated in an initial phase rather than continuously.
Solution Approach 2:
The system performs self-calibration by automatically capturing images of unoccupied seats and generating reference profiles without requiring manual intervention or external calibration equipment. The machine learning algorithm self-adjusts by comparing actual measurements against the generated reference profiles, enabling the system to reduce false positives autonomously while minimizing the time and resources needed for calibration.
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
Achieves high spatial and temporal resolution in detecting seat occupancy, reducing false positives and improving detection accuracy by using multiple height profiles and machine learning for classification.
Implementation Method 1
The imaging device may, for example, be a time-of-flight camera, a stereo camera or a radar camera. The depth data represent, for each pixel, a depth information, which corresponds to a real distance in height.
Data Source
AI summary
Computer implemented method for detecting an occupancy of a seat, comprising capturing, by means of an imaging device, a first image of a seat, the image comprising depth data, determining, by means of a processing device, a first height profile along a first line in the first image from the depth data, and comparing, by means of the processing device, the first height profile with a first reference height profile taken along the same first line to determine whether the seat is occupied.