Stereoscopic Camera Obstruction Detection Using Hybrid Disparity Sectors
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Solution Overview
Problem
Current methods for determining camera obstruction in stereoscopic systems on vehicles suffer from high error rates and require significant processing resources, limiting their effectiveness in detecting partial obstructions and requiring long detection distances.
Innovation Solution
A hybrid approach combining local and semi-global methods for disparity map calculation, using sector-based analysis without energy functions, which calculates a weighted average of obstruction levels based on disparity map density and sector probabilities, allowing for faster and more accurate obstruction detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If image sector analysis method is used for obstruction detection, then the detection can be performed with reduced processing complexity, but the detection precision and reliability are limited with only 75% detection rate for partial obstruction
Solution Approach 1:
The image is divided into multiple sectors, and each sector is processed independently to determine obstruction status. This segmentation allows the system to analyze different regions with appropriate processing depth, improving both reliability and efficiency
Solution Approach 2:
A disparity map is introduced as an intermediary data structure that captures depth information from stereoscopic image pairs. This disparity map serves as the basis for reliable obstruction detection by providing quantitative depth measurements across different image sectors
2Use of energy by moving object
If conventional obstruction detection methods are used, then processing resources are reduced, but the detection distance is limited to average 200 meters and start-up distance of 30 meters
Solution Approach 1:
The system pre-calculates and stores disparity maps for different image sectors before obstruction analysis. This preliminary processing of depth information enables faster and more accurate obstruction detection at longer distances without increasing real-time processing burden
3Loss of information
If disparity map generation with two-step process is used, then depth information is provided for driving assistance, but the processing complexity and computational resources increase significantly
Solution Approach 1:
The disparity map generation and obstruction detection are segmented into independent sector-based processing units. Each sector processes a portion of the image independently, reducing overall computational complexity while maintaining depth information quality for driving assistance applications
Data Source
AI summary
To improve the performance for determining obstruction of a stereoscopic system using two cameras or more, a hybrid of local and semi-global methods is provided. For each stereoscopic image formed from simultaneous left and right images, a breakdown of each left and right image into corresponding sectors is applied. Obstruction level is determined by a disparity map by sector, based on left or right images, and in which a disparity is assigned to each pixel corresponding to the best matching score. A determination of density by sector of the disparity map is carried out by reference to a fraction of pixels with a disparity considered to be valid. A state of obstruction of at least one camera is determined based on a weighted average of the probabilities of obscuring of the sectors of the disparity map obtained by comparison between the density of the sectors and a predefined density level.