Contextual Local Image Recognition Dataset for AR Bandwidth Reduction
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Solution Overview
Problem
Augmented reality devices face data traffic issues due to constant image scanning and three-dimensional model downloading, which can strain limited network bandwidth between devices and AR servers.
Innovation Solution
Implementing a contextual local image recognition dataset module that retrieves a primary content dataset from a server, generates and updates a contextual content dataset based on captured images, and stores it locally, allowing for local recognition of images and downloading of additional information only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant image scanning and server recognition is implemented, then image recognition accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent segments the image recognition system into local and remote components. The device performs local preprocessing and recognition on captured images, while only transmitting unrecognized images or specific data to the server. This segmentation reduces network bandwidth consumption while maintaining recognition accuracy through a hybrid approach.
Solution Approach 2:
The patent implements preliminary action by performing local image preprocessing and recognition attempts on the device before server involvement. The device pre-processes images locally and only sends them to the server when local recognition fails, avoiding unnecessary network transmissions and reducing bandwidth consumption while maintaining accuracy.
2Reliability
If all image data is transmitted to the server, then recognition reliability is improved, but data traffic increases
Solution Approach 1:
The patent extracts and processes only the necessary portions of image data locally before transmission. Instead of sending complete high-resolution images, the device extracts key features or transmits only unrecognized images, reducing data traffic while maintaining recognition reliability through selective data transmission.
Solution Approach 2:
The patent applies partial action by transmitting only a subset of captured images to the server—specifically, those that could not be recognized locally. This partial transmission approach maintains recognition reliability for all images while significantly reducing data traffic compared to transmitting all images.
3Speed
If local dataset is maintained, then response speed is improved, but device storage requirements increase
Solution Approach 1:
The patent applies local quality by maintaining a localized subset of the image recognition dataset on the device, optimized for local processing. This local dataset enables fast response speeds for common images while the device can still access the complete dataset on the server when needed, balancing storage requirements with response speed through differentiated data placement.
Data Source
AI summary
A contextual local image recognition module of a device retrieves a primary content dataset from a server and then generates and updates a contextual content dataset based on an image captured with the device. The device stores the primary content dataset and the contextual content dataset. The primary content dataset comprises a first set of images and corresponding virtual object models. The contextual content dataset comprises a second set of images and corresponding virtual object models retrieved from the server.