Vehicle Interior Camera Segmentation for Child Seat Detection
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Solution Overview
Problem
Existing vehicle interior camera systems rely on expensive and time-consuming human annotation services for object identification, which may involve sharing sensitive passenger data with third parties.
Innovation Solution
A vehicle interior camera system utilizing a Gaussian process image segmentation model to automatically detect and segment target objects, such as a child restraint system, and control vehicle systems based on these detections, reducing the need for human intervention and data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation services are used for object identification, then object detection accuracy can be achieved, but cost and time consumption increase significantly
Solution Approach 1:
The system performs self-annotation by automatically generating masks for target objects using the Gaussian process segmentation model. The vehicle control module extracts image crops, detects passenger masks, segments target objects, and generates training data without human intervention, allowing the system to improve its own performance through self-generated labeled data.
Solution Approach 2:
The Gaussian process image segmentation model acts as an intermediary between raw images and final object detection. It processes image crops and generates accurate target object masks that can be used for training machine learning models, serving as a bridge that eliminates the need for human annotators while maintaining high accuracy.
2Measurement precision
If human annotation services are used for object identification, then object detection accuracy can be achieved, but cost increases significantly
Solution Approach 1:
The system generates its own training data by automatically segmenting target objects and creating masks. The vehicle control module processes images through the Gaussian process model to produce labeled data, eliminating the need to pay third-party annotation companies while maintaining high detection accuracy through self-generated high-quality masks.
Solution Approach 2:
The system uses computational algorithms (Gaussian process segmentation) that can be executed repeatedly at low marginal cost to generate training data. Unlike expensive human annotation services, the computational cost of running the segmentation model is minimal and can be performed locally without recurring payment to external providers.
3Loss of information
If human annotation services are used, then sensitive passenger data must be shared with third parties, but automated methods preserve privacy
Solution Approach 1:
The vehicle control module performs all image processing and mask generation locally within the vehicle system. Sensitive passenger data never leaves the vehicle as the Gaussian process model processes images on-board, eliminating the need to share data with external annotation companies while maintaining full automation of the annotation process.
4Productivity
If automated Gaussian process segmentation is used, then processing speed and privacy are improved, but system complexity increases
Solution Approach 1:
The Gaussian process image segmentation model serves as a specialized intermediary component that handles the complex mathematical computations for mask generation. By isolating this complexity into a dedicated model module, the rest of the system can remain relatively simple while still achieving high processing speed and accuracy through the sophisticated segmentation algorithm.
Data Source
AI summary
An example vehicle interior camera system includes memory configured to store at least one Gaussian process image segmentation model, a vehicle interior camera, and a vehicle control module configured to obtain an image of the interior of the vehicle, extract one or more image crops of a region surrounding a target object in the image, detect and segment a passenger mask in the image, supply the image to the at least one Gaussian process image segmentation model to obtain a region mask associated with the region surrounding the target object, remove the passenger mask from the region mask to extract a target object mask, identify a target object condition according to the target object mask, and control at least one vehicle system or feature in response to identification of the target object condition.