Location-Based Object Recognition for AR Asset Networking
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Solution Overview
Problem
Current image recognition systems face inefficiencies due to reliance on either server-side or client-side processing, with server-side systems experiencing slow response times and client-side systems lacking computation and storage capabilities, limiting their effectiveness in social networking applications.
Innovation Solution
An image recognition system that combines server-side and client-side capabilities, utilizing a shared model production pipeline to extract global features from images, enabling efficient recognition and augmented reality triggers without the need for detailed geometric information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-side processing is used for image recognition, then recognition capabilities are improved, but response time increases and becomes slow
Solution Approach 1:
The patent divides the image recognition system into two segments: a client-side component that performs initial image processing and feature extraction, and a server-side component that handles complex recognition tasks. This segmentation allows the client to quickly preprocess images locally, reducing response time, while the server provides comprehensive recognition capabilities when needed.
Solution Approach 2:
The system performs preliminary image processing and feature extraction actions on the client side before transmitting data to the server. By completing preparatory tasks locally, the system reduces the amount of data that needs to be processed on the server, thereby decreasing overall response time while maintaining recognition accuracy.
2Loss of time
If client-side processing is used for image recognition, then response time is reduced, but computation and storage capabilities are insufficient
Solution Approach 1:
The patent merges client-side and server-side processing capabilities into a unified hybrid system. The client device handles local preprocessing and stores cached data, while the server provides supplementary computation and storage resources. This combination allows the system to achieve fast response times through local processing while overcoming hardware limitations by accessing server resources when needed.
3Measurement precision
If detailed geometric information is extracted from images, then recognition precision is improved, but processing complexity and time increase
Solution Approach 1:
The system extracts only the essential geometric features and visual descriptors needed for recognition, rather than processing all detailed geometric information. By selectively extracting relevant features such as key points, edges, and visual descriptors, the system achieves adequate recognition precision while significantly reducing processing complexity and computational burden.
Data Source
AI summary
The present disclosure describes techniques for social networking based on collecting asset items. Features may be extracted from an image comprising an object. The object may be associated with a location. One or more pre-stored files may be determined based on the location. The one or more pre-stored files may each correspond to one or more objects proximate to the location. Each of the one or more pre-stored files may comprise data indicative of a corresponding object. The object may be recognized based on comparing the features extracted from the image with data comprised in the one or more pre-stored files. An asset item may be received in response to recognizing the object.