Low Light AR Rendering via Infrared Thermal Imaging
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Solution Overview
Problem
Existing assisting systems for field agents fail to render Augmented Reality (AR) content effectively in low light conditions due to the decrease in photon count, making it difficult to compute tasks that rely on environmental visibility.
Innovation Solution
A method and system that utilize a recurrent neural network to identify objects and predict actions in real-time input data from unprocessed image frames, extracting AR objects and rendering them on a user device to assist field agents in low light environments by correlating the data with pre-stored objects and their operational states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional assisting systems use visible light imaging to identify objects and compute tasks, then computation accuracy is improved, but system reliability deteriorates in low light conditions due to insufficient photon count
Solution Approach 1:
The system changes the parameter of light spectrum by using infrared imaging instead of visible light imaging. The infrared camera captures thermal radiation from objects, which is effective in low light conditions where visible light photons are insufficient. This parameter change allows the system to maintain object identification capability in dark environments while preserving computation accuracy.
2Productivity
If the system processes unprocessed image frames in real-time to maintain responsiveness, then productivity is improved, but measurement precision deteriorates due to noise in low light conditions
Solution Approach 1:
The system introduces an intermediary infrared camera that captures thermal radiation patterns from objects. This intermediary device converts the problematic low-light visible light scenario into a useful thermal imaging scenario, where objects emit detectable infrared radiation even in complete darkness. The thermal patterns serve as a mediator that preserves measurement precision while enabling real-time processing.
3Adaptability or versatility
If the system uses pre-stored objects and operational states for correlation matching, then adaptability is improved, but device complexity increases due to the recurrent neural network training and data storage requirements
Solution Approach 1:
The system performs preliminary action by pre-storing object templates and operational states in a database before actual use. The recurrent neural network is trained in advance with labeled thermal imaging data to learn object recognition patterns. This preliminary preparation allows the system to quickly match real-time thermal patterns against the pre-stored database without performing complex training during operation, thus reducing runtime complexity while maintaining high adaptability.
Data Source
AI summary
The present invention discloses a method and a system for rendering content in low light condition for field assistance. The method comprising receiving real-time input data from a user device in a low light condition, identifying at least one object from the real-time input data and corresponding operational state of the at least one object based on a correlation of the at least one object in the input data and corresponding operational state of the at least object with pre-stored objects and corresponding operational state of the pre-stored objects, predicting at least one action to be performed on the identified at least one object, extracting an Augmented Reality (AR) object associated with the identified at least one object on which the selected action is to be performed, and rendering the location, the selected at least one action to be performed, and the AR object on the user device.