Obstacle Detection Using Image Transformation Parameters
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Solution Overview
Problem
Existing roadside sensing systems face accuracy issues in obstacle detection due to camera shake, which is not effectively addressed by current anti-shake methods, leading to inaccurate 3D position calculation of obstacles.
Innovation Solution
A method that determines a transformation parameter between current and template images using reference points to correct for changes in external camera parameters caused by shake, allowing for precise obstacle position calculation in the world coordinate system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If roadside sensing device is mounted on high position, then detection coverage is improved, but device stability deteriorates due to external environment effects
Solution Approach 1:
The system uses reference points on the ground to detect camera position changes and applies transformation parameters to correct the current image, creating a feedback loop that compensates for instability caused by mounting on high positions
Solution Approach 2:
Reference points serve as intermediaries between the camera and the ground plane, enabling the system to measure and correct for camera position changes without requiring the camera to be physically stable
2Adaptability or versatility
If camera position changes due to shake, then device adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The system continuously monitors reference point positions to detect camera shake and applies real-time correction through transformation parameters, maintaining measurement precision despite position changes
Solution Approach 2:
The system creates a transformed copy of the current image that aligns with the template image coordinate system, allowing accurate measurement even when the original image is distorted by camera shake
3Measurement precision
If transformation correction is applied, then obstacle detection precision is improved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical stabilization mechanisms with computational image transformation methods, achieving precision correction through software rather than hardware complexity
Data Source
AI summary
A method for detecting an obstacle, an electronic device, a roadside device and a cloud control platform are provided and relates to the fields of automatic drive and intelligent traffic. The method includes acquiring a current image, wherein the current image includes an obstacle object representing an obstacle located on a predetermined plane; determining a transformation parameter between the current image and a template image based on current coordinates of a plurality of reference points on the predetermined plane in the current image and template coordinates of corresponding reference points for the plurality of reference points in the template image; and determining a position of the obstacle in a world coordinate system utilizing the transformation parameter and pixel coordinates of the obstacle object in the current image.