Extendable AR System Plugins for Object Recognition
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Solution Overview
Problem
Conventional augmented reality systems are limited in their ability to identify and provide supplemental content for multiple objects in a user's environment without prior domain specification, relying on pre-configured applications and lacking extensibility to incorporate third-party data and functionality.
Innovation Solution
An extendable augmented reality system architecture that allows third-party data and functionality to interact with the system, using plugins to recognize objects and provide supplemental content through sensor data analysis, enabling concurrent and heterogeneous identification experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional augmented reality systems use pre-configured applications for object identification, then the system can provide basic recognition functionality, but the system lacks extensibility to incorporate third-party data and functionality
Solution Approach 1:
The system is divided into independent modules: a core AR platform and separate plugin components. Each plugin is a self-contained module that can be independently developed, installed, and removed. This segmentation allows third-party developers to create specialized recognition plugins without modifying the core system, thereby improving extensibility while maintaining manageable complexity through clear module boundaries.
Solution Approach 2:
The core AR platform provides universal functionality that serves multiple purposes: it handles sensor data acquisition, manages plugin registration and execution, and coordinates between different recognition plugins. This universal platform enables the system to support various object identification tasks (text, images, audio, video) through a single unified architecture rather than requiring separate specialized systems for each function.
2Adaptability or versatility
If the system supports multiple object identification types through plugins, then the system can provide diverse recognition capabilities, but the system complexity increases due to managing multiple plugins and data sources
Solution Approach 1:
The core AR platform acts as an intermediary layer between the user and multiple plugin components. It provides standardized interfaces for plugin registration, data submission, and result retrieval. This mediator approach allows diverse plugins (text recognition, image recognition, audio recognition) to communicate with the system through uniform protocols, reducing the complexity of managing multiple different data sources and formats.
Solution Approach 2:
Plugins are registered and configured in advance with the core system, establishing data flow paths and interaction protocols before actual object recognition tasks begin. This preliminary setup includes defining what sensor data each plugin processes, how results are formatted, and what user actions are triggered. By performing this configuration beforehand, the system avoids complex runtime decision-making and simplifies the management of multiple plugins during operation.
3Adaptability or versatility
If the system allows dynamic plugin installation and activation, then users can customize identification capabilities, but the ease of operation decreases due to plugin configuration and management
Solution Approach 1:
The system automatically handles plugin registration, data routing, and result integration without requiring manual configuration by users. When a plugin is installed, the core platform automatically detects it, registers its capabilities, and configures the appropriate data flow paths. This self-service approach allows users to simply install plugins through an app store or package manager, while the system manages the complex configuration details automatically, thereby maintaining ease of operation while enabling customization.
4Adaptability or versatility
If the system processes sensor data through multiple plugins concurrently, then the system can provide comprehensive object identification, but the processing time and energy consumption increase
Solution Approach 1:
The system employs selective plugin execution where not all plugins are activated for every recognition task. The core platform determines which plugins are relevant based on the current sensor data type and user context, activating only the necessary subset of plugins. For example, if the sensor data is audio, only audio recognition plugins are executed, while image recognition plugins remain inactive. This partial action approach maintains comprehensive identification coverage across different modalities while reducing energy consumption by avoiding unnecessary processing.
Data Source
AI summary
An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.