Vehicle Interior Depth Estimation Using Mono-Camera and Neural Networks
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Solution Overview
Problem
Current depth estimation methods using mono-camera images in vehicles provide only relative, qualitative depth estimates, which are insufficient for high-precision applications, such as vision-based passenger airbag control and other safety systems, requiring absolute depth information like that from time-of-flight or stereo cameras.
Innovation Solution
A method and system for determining absolute depth in a vehicle's interior using an imaging system, which involves identifying points of interest, generating a reference depth map, and refining it using machine learning techniques, including neural networks, to provide accurate depth estimates for passenger safety and vehicle control functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art machine learning techniques are used to estimate depth from a 2D mono-camera image, then the system complexity is reduced and cost is lowered, but only relative, qualitative depth estimates can be achieved which are insufficient for high precision applications
Solution Approach 1:
The patent introduces an intermediary process that takes a 2D mono-camera image and generates a depth map through multi-step processing including point of interest detection, 3D coordinate transformation using camera calibration parameters, and machine learning-based refinement. This intermediary depth map generation process enables absolute depth estimation without requiring complex hardware like ToF or stereo cameras, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent performs preliminary actions by pre-detecting points of interest in the 2D image, pre-calculating their 3D coordinates using camera calibration parameters, and pre-generating a reference depth map before final refinement. These preliminary steps prepare the data in advance, enabling the subsequent machine learning model to focus on refinement rather than complete depth estimation from scratch, thereby achieving high precision with moderate system complexity
2Measurement precision
If ToF or stereo cameras are used to obtain absolute depth estimates, then depth estimation precision is improved for high precision applications, but the device complexity and cost increase
Solution Approach 1:
The patent creates a virtual depth map (a digital copy of depth information) from a 2D image through computational processes including point of interest detection, 3D coordinate transformation, and machine learning refinement. This computational copy of depth information replicates the functionality of expensive ToF or stereo cameras without requiring their complex hardware, thus achieving absolute depth estimation with reduced device complexity
Solution Approach 2:
The patent replaces the mechanical/optical depth sensing systems (ToF cameras or stereo cameras) with a computational approach using a standard 2D mono-camera combined with image processing and machine learning algorithms. This substitution eliminates the need for complex depth-sensing hardware while maintaining the capability to generate absolute depth estimates, thereby resolving the contradiction between measurement precision and device complexity
3Device complexity
If only 2D camera images are used for interior sensing, then the system is simpler and cheaper, but confidence in the spatial position of people, animals and objects is reduced
Solution Approach 1:
The patent transforms 2D image data into 3D spatial information by detecting points of interest in the 2D image, calculating their 3D coordinates using camera calibration parameters, and generating a depth map that represents the third dimension. This dimensionality transformation recovers spatial position information that would otherwise be lost in 2D imaging, thus reducing information loss while maintaining simple 2D camera hardware
Data Source
AI summary
A computerized method of depth determination in a vehicle includes determining, based on image data obtained by an imaging system representing at least a part of an interior of the vehicle, one or more points of interest. The method includes determining one or more characteristics associated with each determined point of interest. The one or more characteristics includes a location and/or dimensions of each determined point of interest. The method includes generating, based on the determined points of interests and the associated characteristics, a reference depth map of the vehicle interior represented in the image data. The method includes generating, based on the image data and the reference depth map, a refined reference depth map.