Traffic Camera Viewpoint Determination via 3D Road Modeling
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Solution Overview
Problem
Existing traffic cameras face challenges in accurate calibration due to rotation, panning, and zooming, making it difficult to establish precise mapping between pixel coordinates and physical road surface coordinates, which is crucial for deriving meaningful information from computer vision algorithms and managing traffic incidents.
Innovation Solution
A system and method for determining a traffic camera's viewpoint by segmenting road surfaces from captured images, generating a 3D model from geographical data, adding a simulated camera to the model, and selecting the best-fit simulated image to create a mapping between pixel and real-world locations, accounting for lens distortions and camera movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then the process is simple, but the mapping precision between pixel coordinates and physical road surface coordinates deteriorates due to camera rotation, panning, and zooming
Solution Approach 1:
The patent creates a virtual 3D copy of the road environment using geographical data and generates simulated images from multiple virtual camera positions. These simulated images serve as references to calibrate the actual camera, allowing precise mapping without complex physical calibration procedures. The virtual model replicates the real world geometry to enable accurate coordinate transformation.
Solution Approach 2:
The patent transitions from 2D image processing to 3D spatial modeling by creating a three-dimensional virtual representation of the road. This dimensional change enables the system to account for camera movements in multiple directions (panning, tilting, zooming) and establish accurate mappings between pixel coordinates and physical locations through volumetric spatial relationships.
2Area of stationary object
If the camera performs pan-tilt-zoom operations, then the coverage area is improved, but the calibration accuracy deteriorates
Solution Approach 1:
The patent creates a dynamic calibration system where the virtual 3D model and simulated images are continuously updated to reflect the camera's current position and orientation. As the camera performs pan-tilt-zoom operations, the system recalibrates by comparing new simulated images with actual captured images, maintaining calibration accuracy throughout the dynamic coverage area expansion.
Solution Approach 2:
The system uses feedback from comparing simulated images (generated from the virtual 3D model) with actual captured images to continuously refine and update the calibration parameters. This feedback loop ensures that calibration accuracy is maintained even as the camera coverage area changes through pan-tilt-zoom operations.
3Area of stationary object
If multiple cameras are deployed, then the coverage is improved, but the system complexity increases
Solution Approach 1:
The patent creates a universal virtual 3D model that serves multiple functions simultaneously: it acts as a geometric reference for calibration, a spatial mapping database for coordinate transformation, and a coverage analysis tool for optimizing camera placement. This multi-functional virtual model reduces overall system complexity by consolidating multiple purposes into a single framework that can handle multiple cameras uniformly.
Data Source
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AI summary
A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.