Vehicle Vision System Self-Calibration via Object Position Tracking
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Solution Overview
Problem
Existing vision systems for motor vehicles face challenges in accurately estimating the extrinsic calibration of imaging devices of different types or wavelength ranges, leading to increased computational costs and risks of false detections due to low precision in camera calibration.
Innovation Solution
The system uses the position of detected objects for calibration estimation, leveraging existing object detectors to track and store object positions over time, allowing for systematic deviation analysis and adjustment of camera calibration parameters, independent of imaging device type or viewpoint, thereby optimizing calibration for object detection applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods (vanishing point detection, optical flow, eight-point algorithm) are used, then calibration can be performed, but computational cost increases and detection reliability decreases due to larger search areas and false detections
Solution Approach 1:
The system uses detected objects themselves to provide calibration information, eliminating the need for separate calibration procedures or artificial markers. The object detector automatically provides both detection results and calibration data, making the system self-calibrating and reducing computational overhead.
Solution Approach 2:
The object detector serves dual purposes: detecting objects for navigation and simultaneously providing calibration data for the imaging system. This multi-functionality eliminates the need for separate calibration procedures and reduces overall system complexity.
2Measurement precision
If traditional calibration methods are used, then calibration can be achieved, but detection reliability decreases due to false detections and erroneous associations in larger search areas
Solution Approach 1:
The system uses feedback from detected object positions to continuously refine and update calibration parameters. By comparing expected object positions with actual detections and adjusting calibration accordingly, the system maintains high detection reliability while using smaller, more focused search areas.
3Measurement precision
If calibration is performed using standardized objects (traffic signs, guide posts), then calibration can be achieved, but the system becomes dependent on specific object types and viewpoints
Solution Approach 1:
The calibration method works with any detectable object type regardless of imaging device characteristics. The system adapts to different camera types, wavelengths, and viewpoints by using the generic properties of detected objects rather than relying on specific standardized objects, making it universally applicable.
Data Source
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AI summary
A vision system (10) for a motor vehicle comprises an imaging means (11) with a plurality of imaging devices (12.1, 12.2) for acquiring images from a surrounding of a motor vehicle, and a processing means (14) adapted to perform image processing on images from said imaging devices (12.1, 12.2). The processing means comprises an object detection means (20) adapted to detect objects in the surrounding of the motor vehicle based on image data from said imaging means (11), and a calibration means (21) adapted to estimate the extrinsic calibration of at least one of said imaging devices (12.1, 12.2). The calibration means (21) is adapted to perform said estimation using the position of detected objects provided by said object detection means (20).