Vehicle Seat Image Comparison for Child Seat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current seat occupancy classification systems in vehicles face challenges in reliably distinguishing between a child seat occupied by a large child and an unoccupied seat, due to limited training data and similarities in image features, leading to potential misclassification.
Innovation Solution
A computerized method and system that utilizes a pre-trained machine learning classification network, specifically a Siamese neural network, to process current and reference images of vehicle seats, determining the seat state by comparing embeddings and confidence scores to accurately identify whether a child seat is mounted, even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning algorithms are used with limited training data, then the system can operate with available data, but misclassification errors occur between child seats and unoccupied seats
Solution Approach 1:
The system performs preliminary actions by capturing and storing reference images of unoccupied seats before actual classification tasks. These reference images are stored in a database and used during runtime comparison, allowing the system to achieve reliable classification without requiring extensive training data during the machine learning training phase
Solution Approach 2:
The system creates copies of seat images at different states (occupied and unoccupied) and stores them as reference images. During classification, the current seat image is compared against these stored reference images using image processing techniques, enabling accurate differentiation without needing large volumes of diverse training data
2Measurement precision
If image processing is used to detect child seats, then visual information can be analyzed, but misclassification occurs when large children resemble unoccupied seats
Solution Approach 1:
The system segments the classification task into distinct phases: capturing reference images of unoccupied seats, processing current seat images, comparing the two sets of images, and making classification decisions. This segmentation allows each phase to be optimized independently, improving detection accuracy while managing complexity through modular processing steps
Solution Approach 2:
The system introduces reference images as an intermediary element between the raw current seat image and the final classification decision. By comparing the current image against stored reference images of known unoccupied seats, the system creates an intermediate comparison stage that enhances detection precision and reduces misclassification
Data Source
Figure 1~2
Figure 3
Figure 4A~4D
AI summary
A computerized method of child seat detection for a seat occupancy classification system in a vehicle is presented. The method comprises receiving a current image showing a region of a vehicle seat currently captured inside of the vehicle and retrieving one or more reference images, wherein at least one reference image is a previously stored first reference image of the vehicle seat being unoccupied. The method further comprises determining a seat state of the vehicle seat by processing the current image and one or more reference images with a pre-trained machine learning classification network and determining, based on an output from the machine learning classification network, the seat state indicating at least whether the vehicle seat in the current image comprises a mounted child seat. Finally, the method forwards the seat state to the seat occupancy classification system. A corresponding system, vehicle, and computer program are also presented.