Vehicle Passenger Detection Using Adaptive Load Thresholds
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Solution Overview
Problem
Existing passenger detection systems face challenges in accurately determining the presence of a passenger on a vehicle seat due to false positives from baggage weight and unreliable imaging conditions.
Innovation Solution
A passenger detection device and program that combine load sensor data with face detection from an imaging device, adjusting a threshold value based on the face determination degree to differentiate between passenger and baggage, ensuring accurate passenger presence determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a load sensor is used to detect passenger presence, then the detection system is simple and fast, but it produces false positives when baggage is placed on the seat
Solution Approach 1:
The patent combines load sensor detection with imaging device detection to create a hybrid detection system. The load sensor provides rapid response while the imaging device verifies actual passenger presence by detecting facial features, thereby eliminating false positives from baggage while maintaining fast detection capability.
Solution Approach 2:
The imaging device acts as an intermediary verification mechanism. When the load sensor detects a potential passenger, the imaging device captures an image and analyzes facial features to confirm whether the load is indeed a passenger or merely baggage, thus mediating between the simple load detection and accurate passenger identification.
2Measurement precision
If a camera is used to detect passengers, then visual confirmation is obtained, but detection accuracy varies with imaging conditions such as lighting and angle
Solution Approach 1:
The system dynamically adjusts the threshold value for face detection based on imaging conditions. When lighting is poor or imaging angles are suboptimal, the threshold is adjusted to maintain detection accuracy. This allows the system to adapt to varying imaging conditions while preserving the accuracy benefits of visual confirmation.
Solution Approach 2:
The system uses feedback from the imaging conditions themselves to adjust its detection parameters. By monitoring image quality metrics such as lighting levels and detection confidence, the system adjusts the threshold value to maintain optimal detection accuracy across different imaging conditions.
3Ease of operation
If a fixed threshold value is used for load-based detection, then the system is simple to operate, but it cannot adapt to different scenarios such as baggage versus passenger loads
Solution Approach 1:
The system transitions from a static fixed threshold to a dynamic adaptive threshold. The threshold value automatically adjusts based on real-time imaging data and detection confidence levels. This maintains operational simplicity for the user while enabling the system to adapt to different scenarios such as distinguishing between baggage and passenger loads.
Data Source
AI summary
A passenger detection device includes: an input unit that receives a captured image of an interior space of a vehicle imaged by an imaging device and a load measured by a load sensor provided on a seat provided in the interior space of the vehicle; a determination unit that determines that a passenger is seated on the seat, in a case where the load is equal to or more than a predetermined threshold value; a detection unit that obtains a face determination degree indicating a certainty of a face of the passenger in the captured image from the captured image; and a threshold value changing unit that changes the threshold value according to the face determination degree.