Pulsing Light Source Recognition Using Multi-Integration Sensor
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Solution Overview
Problem
Existing methods for recognizing pulsing light sources, such as LED headlights, face challenges due to their unstable and non-coherent patterns, especially when pulse frequencies exceed the camera's read-out rate, leading to unreliable recognition and classification in driver assistance systems.
Innovation Solution
A method utilizing a light-sensitive sensor with multiple integration intervals and predefined gray values, where the gray value of each picture point is defined as the sum of charge levels from different integration intervals, weighted by corresponding factors, to identify pulsing light sources by examining gray values within specific vicinities defined around zero and saturation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a standard camera sensor with single integration interval is used to detect light sources, then the detection process is simple and fast, but pulsing light sources cannot be reliably recognised when pulse frequency exceeds read-out rate
Solution Approach 1:
The integration period is divided into multiple integration intervals (first and second integration intervals) within a single image acquisition cycle. This segmentation allows the sensor to capture light intensity at different time points, enabling detection of pulsing light sources whose frequency exceeds the camera read-out rate. The gray value is calculated as a weighted sum of charge levels from these segmented intervals.
Solution Approach 2:
The sensor performs periodic measurements of light intensity by conducting multiple integration intervals repeatedly within each image cycle. This periodic sampling at different phases allows capture of pulsing light patterns, transforming the harmful high-frequency pulsing into detectable temporal variations in gray values.
2Reliability
If multiple integration intervals are used to detect pulsing light sources, then recognition reliability improves, but calculation complexity and processing time increase
Solution Approach 1:
The evaluation unit calculates gray values by changing parameters - specifically using weighted sums of charge levels from different integration intervals. By adjusting weights and comparing against predefined thresholds, the system identifies pulsing patterns without requiring complex algorithms. The gray value formula transforms multiple charge level measurements into a single comparative parameter.
Solution Approach 2:
The method creates a simplified representation (gray value) that copies essential information from multiple charge level measurements. This gray value serves as a compressed descriptor that captures pulsing characteristics, allowing complex temporal patterns to be evaluated through simple threshold comparisons rather than full waveform analysis.
3Reliability
If image regions are formed from picture points with varying gray values, then comprehensive light source detection is achieved, but unstable image regions arise from pulsing light sources making them difficult to track
Solution Approach 1:
The system performs preliminary classification by examining gray values against predefined thresholds before forming or tracking image regions. By identifying picture points with gray values indicating pulsing light sources in advance, the system can apply special tracking rules or exclude these regions from standard tracking algorithms, preventing instability from propagating through the recognition system.
Solution Approach 2:
The method introduces dynamic adaptation in image region formation by allowing region boundaries and properties to adjust based on gray value patterns across multiple integration intervals. Image regions corresponding to pulsing light sources are handled with dynamic thresholds and adaptive tracking parameters, enabling the system to maintain stable region definitions despite temporal gray value variations.
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 approach enhances the reliability and accuracy of recognizing pulsing light sources, even at high pulse frequencies, by stabilizing image regions and reducing false positives, thereby improving the performance of driver assistance systems.
Implementation Method 1
gray values of picture points of the individual images are measured with a light-sensitive sensor of the camera
Data Source
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AI summary
Summary The invention relates to a method and a device for recognising a pulsing light source (7), in which a sequence of individual images is acquired with a light sensitive sensor (5). In an evaluation unit (6), all or at least some of the picture points of each individual image are examined with regard to at least one predefined feature. A gray value of any picture point depends on two or more charge levels of at least one of the photodetectors of the sensor (5) corresponding to the picture point at the ends of at least two integration intervals for the picture point. The at least two integration intervals for said picture point have different lengths. The examination comprises an examination of the picture points as to whether the picture points have gray values in a defined vicinity of one of at least one predefined gray value.