Stereo Camera Optical Self-Diagnosis Using Disparity Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera systems require high computing power and memory for optical self-diagnosis, making them inefficient in detecting dirt accumulation and image quality impairments, especially in monitoring systems like person counting systems.
Innovation Solution
A method for optical self-diagnosis using stereo images to produce a depth image through disparity maps, reducing data processing complexity and enabling quicker evaluation by determining valid disparity values and outputting warning signals based on threshold comparisons, with an installation phase for calibration and consideration of ambient environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gray-scale pictures are used for optical self-diagnosis, then image quality assessment is possible, but computing power and memory requirements become excessively high
Solution Approach 1:
The invention extracts only the essential information needed for self-diagnosis by using disparity maps from stereo imaging rather than processing complete gray-scale pictures. This extraction approach isolates the depth information that is most relevant for detecting dirt accumulation and optical impairments, eliminating the need to process redundant image data and thereby reducing computing power and memory requirements while maintaining diagnosis reliability
Solution Approach 2:
The invention transitions from two-dimensional gray-scale image processing to utilizing depth information in a third dimension through disparity maps. By working with depth data derived from stereo parallax rather than intensity data, the system achieves more efficient processing for detecting optical impairments, as depth information provides direct geometric constraints that simplify the analysis compared to full image processing
2Measurement precision
If complete image evaluation is performed, then detection accuracy is high, but processing time increases significantly
Solution Approach 1:
The invention extracts only the disparity information from stereo images that is necessary for detecting optical impairments, rather than evaluating complete images. This selective extraction of depth data maintains detection accuracy for dirt accumulation and masking while dramatically reducing processing time by avoiding computation of redundant image features
Solution Approach 2:
The system performs preliminary stereo matching to generate disparity maps before conducting the actual self-diagnosis evaluation. This preliminary action pre-processes the image data into a form that is optimized for detecting optical impairments, so that the subsequent diagnosis step can proceed quickly with already-processed depth information rather than raw images
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for reliable self-diagnosis with reduced computational requirements, effectively detecting soiling, adverse light conditions, and lens masking, while adjusting to different environments and excluding transient disturbances, thus improving image quality and detection accuracy in monitoring systems.
Implementation Method 1
The production of the depth image occurs from the first and the second partial images on the basis of parallax(es) between mutually corresponding pixels from the partial images. Disparity values may be determined as the measure for the parallax.
Data Source
AI summary
The present invention relates to a method for the optical self-diagnosis of a camera system and to a camera system for carrying out the method. The method comprises recording stereo images obtained from in each case at least two partial images (2, 3) creating a depth image, that is to say a disparity map (5) given by calculated disparity values, determining a number of valid disparity values (6) of the disparity map (5), and outputting a warning signal depending on the number of valid disparity values determined. A device for carrying out such a method comprises a stereo camera (1) having at least two lenses (7,8) and image sensors, an evaluation unit and a display unit.