Vehicle Image Spatial Characterization via Splitting Lines
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Solution Overview
Problem
Current methods for object detection, such as those using neural networks, face challenges in reliably performing three-dimensional reconstruction of vehicles, making spatial characterization of detected vehicles complex and unreliable.
Innovation Solution
A method employing a vehicle-mounted processing device and camera to record image information, using machine learning-based evaluation means, such as neural networks, to determine bounding boxes and splitting lines for spatial characterization, enabling reliable three-dimensional reconstruction of external vehicles by partitioning vehicle images into regions and estimating their orientation and position relative to the ego vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If neural networks are used for object detection to determine bounding boxes, then detection capability is improved, but three-dimensional reconstruction reliability deteriorates
Solution Approach 1:
The patent divides the vehicle image into multiple quadrilateral regions (front, rear, left side, right side) using splitting lines. This segmentation allows each region to be independently characterized for spatial reconstruction, improving reliability by breaking down the complex 3D reconstruction problem into manageable parts while maintaining the benefits of neural network-based detection for bounding box determination.
2Measurement precision
If multiple quadrilateral shapes are used to characterize vehicle regions, then spatial characterization detail is improved, but system complexity increases
Solution Approach 1:
The patent segments the vehicle bounding box into four quadrilateral regions (front, rear, left side, right side) using splitting lines that extend from the bounding box center to each corner. This provides detailed spatial characterization of vehicle posture while maintaining manageable system complexity through a systematic division approach.
Solution Approach 2:
The patent transitions from two-dimensional bounding box detection to three-dimensional spatial characterization by introducing depth information through quadrilateral region analysis. The splitting lines and quadrilateral shapes enable inference of vehicle orientation and distance, adding a third dimension to the characterization without excessive complexity.
Data Source
AI summary
A method is provided for the spatial characterization of at least one vehicle image of image information, wherein the image information encompasses the vehicle image of an external vehicle and an environment image of an environment of the external vehicle. The method comprises: determining a bounding box for the vehicle image, in order to use the bounding box for a delimiting of the vehicle image from the environment image, determining a splitting line for the bounding box, in order to use the splitting line for a partitioning of the vehicle image into at least two vehicle sides, determining the spatial characterization with the aid of the bounding box and the splitting line, wherein at least one evaluation means based on machine learning, especially a neural network, is used for the determining of the bounding box and the splitting line.