Vehicle Seat Occupancy Detection with Region-Based Image Processing
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Solution Overview
Problem
Existing imaging devices for detecting seat occupancy in vehicle cabins consume significant computing power and are unreliable.
Innovation Solution
A computer-implemented method using an imaging device to capture vehicle cabin images, identify seat characteristics, determine regions associated with the seats, and process information to detect occupancy status, utilizing machine-learning models trained on various vehicle cabin types to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imaging devices are used to detect seat occupancy, then detection capability is provided, but computing power consumption increases and reliability decreases
Solution Approach 1:
The patent segments the seat detection task by identifying only specific characteristic points (headrest, seat back, seat cushion) rather than processing the entire seat image. This segmentation approach reduces the computational burden while maintaining detection reliability, as the system only needs to detect the presence or absence of these key features to determine occupancy status.
Solution Approach 2:
The patent extracts only the essential characteristic points from the seat image (headrest, seat back, seat cushion) and uses these extracted features for occupancy detection. By taking out only the necessary information rather than processing the complete image data, the system reduces computing power consumption while preserving detection accuracy.
2Measurement precision
If detailed image processing is performed to improve detection accuracy, then detection precision improves, but computing power consumption increases
Solution Approach 1:
The patent extracts only the essential characteristic points (headrest, seat back, seat cushion) from the seat image and uses these extracted features for occupancy detection. By taking out only the necessary information rather than processing the complete image data, the system reduces computing power consumption while preserving detection accuracy.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific critical regions (headrest, seat back, seat cushion) rather than uniformly processing the entire image. This localized approach ensures high detection accuracy for occupancy detection while minimizing overall computing power consumption by ignoring non-essential image areas.
Data Source
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AI summary
Computer implemented method for detecting an occupancy of a seat within a vehicle cabin, the method comprising capturing, by means of an imaging device, an image of the vehicle cabin, identifying, by means of the processing device, a set of characteristics associated with a seat in the captured image, determining, by means of the processing device, a region associated with the identified seat based on the set of characteristics, wherein the region is configured to cover at least a portion of the seat; and determining a seat occupancy status of the seat by processing information obtained from the corresponding region.