Light Source Identification via Rolling Shutter Stripe Energy Spectrum
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Solution Overview
Problem
Current motion tracking systems face challenges in identifying moving objects based on the characteristics of light sources, particularly in distinguishing between different light sources in a dynamic environment.
Innovation Solution
A method and device that detect M stripe sets in an image using a rolling shutter image sensor, obtain energy spectrum data, and calculate correlation coefficients between the image data and database energy spectrum data to determine the identity of each light source, enabling accurate tracking of moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to capture light sources, then the system can identify moving objects, but the accuracy of distinguishing between different light sources in a dynamic environment deteriorates
Solution Approach 1:
The patent divides the light source identification task into multiple segments: capturing stripe sets at different times, extracting energy spectrum data from each stripe set, and comparing with database entries. This segmentation allows the system to handle complex dynamic environments by processing information in manageable chunks, improving both accuracy and distinguishability of different light sources.
Solution Approach 2:
The patent transforms the identification problem from the spatial domain to the frequency domain by extracting energy spectrum data. This dimensional change enables the system to distinguish between different light sources based on their spectral characteristics rather than just spatial position, significantly improving identification accuracy in dynamic environments.
2Measurement precision
If correlation coefficient analysis is performed between all light sources and stripe sets, then identification accuracy improves, but calculation complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-extracting energy spectrum data for all possible light sources and storing them in a database before actual identification occurs. This pre-processing reduces the computational burden during real-time identification, as the system only needs to compare extracted stripe set data against pre-computed database entries rather than analyzing all possibilities from scratch.
Solution Approach 2:
The patent creates a database copy of energy spectrum data for known light sources, allowing the system to compare extracted stripe set characteristics against this reference copy. This copying approach enables accurate identification without requiring complex real-time analysis of all possible light source combinations, reducing computational complexity while maintaining high accuracy.
Data Source
AI summary
The present disclosure provides a method and device for identifying a light source. The method includes as follows. M stripe sets in an image may be detected. A first energy spectrum data corresponding to each of M stripe sets may be obtained. A second energy spectrum data corresponding to each light source in a database may be obtained. The database may include K light sources, and the energy spectrum data corresponds to an identity of the light source. A correlation coefficient between the second energy spectrum data and the first energy spectrum data may be calculated to obtain M*K correlation coefficients. The identity of each stripe set corresponding to the light source in the database may be determined according to the M*K correlation coefficients. With this disclosure, the tracking of the light source emitted by a controller can be achieved.