Auto-Calibrating Vehicle Camera Using Trackable Object Key Points
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Solution Overview
Problem
Existing autonomous driving systems face challenges in efficiently processing continuous image frames for object detection and tracking, leading to limitations in recognizing targets and providing accurate bounding box generation, distance estimation, and auto-calibration.
Innovation Solution
A method and apparatus for object detection and tracking in autonomous driving systems, which involves obtaining image frames, determining key points and descriptors, and generating bounding boxes based on these key points and descriptors, while also enabling distance estimation and auto-calibration through spatial information and non-volatile memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and tracking of moving targets is performed, then tracking accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The patent replaces manual identification and tracking with automated computer vision algorithms that process image frames to detect and track objects. The system uses algorithms to automatically identify key points, generate bounding boxes, and estimate distances without human intervention, thereby maintaining high accuracy while significantly improving processing speed.
Solution Approach 2:
The system performs self-service by automatically processing continuous image frames to detect, track, and calibrate objects without requiring manual intervention. The automated algorithms continuously analyze image data, update object positions, and perform calibration tasks autonomously, achieving both high accuracy and efficient processing.
2Productivity
If automated processing algorithms are used for bounding box generation and distance estimation, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously refines its bounding box generation and distance estimation algorithms based on processed image data. The automated processing uses feedback from key point detection and descriptor matching to improve measurement precision while maintaining high processing speed through efficient algorithmic iterations.
Solution Approach 2:
The system performs preliminary actions by pre-processing image frames to identify key points and extract descriptors before generating bounding boxes and estimating distances. This preliminary processing organizes data in advance, enabling the automated algorithms to achieve high measurement precision efficiently during the main processing stage.
3Measurement precision
If camera calibration is performed manually, then calibration accuracy is improved, but time consumption deteriorates
Solution Approach 1:
The patent replaces manual camera calibration with automated calibration algorithms that use processed image frames and detected key points to determine camera parameters. The system automatically performs calibration tasks by analyzing spatial relationships between detected objects and image coordinates, achieving high calibration accuracy while dramatically reducing calibration time.
Solution Approach 2:
The system performs self-calibration by automatically using its own detected objects and image processing results to calibrate the camera. The automated calibration process utilizes feedback from the object detection and tracking algorithms to refine camera parameters without requiring separate manual calibration procedures, thereby achieving accurate and rapid calibration.
4Reliability
If continuous image frames are processed for object detection and tracking, then tracking reliability is improved, but computational complexity deteriorates
Solution Approach 1:
The patent extracts only the essential features from continuous image frames for object detection and tracking, such as key points and descriptors, rather than processing all image data. By taking out and focusing on critical information elements, the system maintains high tracking reliability through continuous monitoring while significantly reducing computational complexity.
Solution Approach 2:
The system segments the image processing task into distinct stages: key point detection, descriptor extraction, matching, and tracking. This segmentation allows each stage to process only relevant data with optimized algorithms, improving tracking reliability through systematic processing while reducing overall computational complexity by avoiding redundant calculations.
Data Source
AI summary
A method for calibration of a camera of a vehicle is disclosed. The method includes: obtaining a first image frame by the camera including an object; obtaining a second image frame by the camera including the object; determining a first set of key points of the object in the first image frame; determining a second set of key points of the object in the second image frame; obtaining a first spatial information about the first set of key points from a non-volatile memory; obtaining a second spatial information about the second set of key points from the same or a different non-volatile memory; and auto-calibrating the camera based on the first and the second spatial information.