Augmented Reality Display Screen Obfuscation via Tethered Processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently capturing and processing digital images, particularly in augmented reality systems, due to computational intensity and power constraints, leading to high latency and increased power consumption.
Innovation Solution
The system employs a wearable eyewear device with a display system and machine learning model to detect and modify display screens, utilizing a wireless tethered mode with a base device for processing, and implementing augmented reality content generation and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image processing is performed on the eyewear device itself, then processing speed and responsiveness are improved, but power consumption and computational burden increase significantly
Solution Approach 1:
The system divides the processing workload between the eyewear device (capturing images, detecting display screens using machine learning model, selecting regions) and the base device (receiving image data, processing the selected regions, generating modified image data). This segmentation allows the eyewear device to perform only lightweight operations locally while offloading intensive processing to the base device, thereby reducing power consumption while maintaining processing speed.
Solution Approach 2:
The base device acts as an intermediary between the eyewear device and the final output. The eyewear device captures images and detects display screens, then transmits selected regions to the base device for processing. The base device processes the image data and generates modified image data, which is then transmitted back to the eyewear device for display. This intermediary approach reduces the computational burden on the eyewear device while maintaining processing speed.
2Productivity
If more computational resources are allocated to the eyewear device for real-time processing, then processing capability is improved, but device complexity and power consumption increase
Solution Approach 1:
The system extracts the intensive processing functions from the eyewear device and relocates them to the base device. The eyewear device retains only essential functions (image capture, machine learning-based display screen detection, region selection), while the base device handles the computationally intensive tasks (processing selected regions, generating modified image data). This extraction reduces device complexity while maintaining processing capability.
3Use of energy by moving object
If image processing is offloaded to an external base device, then power consumption is reduced, but latency may increase due to wireless communication
Solution Approach 1:
The eyewear device performs preliminary actions (capturing images, detecting display screens using machine learning model, selecting regions of interest) before transmitting data to the base device. This preliminary processing reduces the amount of data that needs to be transmitted and processed remotely, thereby reducing overall latency while maintaining power efficiency. The base device then processes only the selected regions, further reducing processing time.
Data Source
AI summary
The subject technology receives first image data captured by a camera of an eyewear device. The subject technology detects, using a machine learning model, a representation of a display screen in the first image data. The subject technology selects at least a portion of the representation of the display screen. The subject technology adjusts a visual appearance of the portion of the representation of the display screen. The subject technology causes display of the adjusted visual appearance using a display system of the eyewear device.