Introduction to cloud image recognition
This article introduces the core principles, expected effects, and differences from planar image tracking of EasyAR CRS (Cloud Recognition Service), helping developers understand the applicable scenarios and limitations of cloud recognition.
Basic principles
Cloud Recognition is a solution that migrates the recognition process to the cloud, suitable for scenarios with a large target library or requiring dynamic updates. Its core workflow is as follows:
Gallery management
- Create a gallery: Upload your target images on the CRS console. The system will automatically calculate the visual features of the target images and add them as a Target to the backend database.
- CRUD operations: On the CRS console, you can perform CRUD (Create, Read, Update, Delete) operations on your target library. After the operations, clients can use it without updating the application.
Important
The requirements for image quality in cloud recognition are exactly the same as those in planar image tracking. For details, refer to: Best practices for target images.
Recognition process
- Image upload: The client captures the current scene through the camera and sends the image data to the EasyAR CRS service.
- Cloud matching: The server quickly searches the cloud target library to match the pre-stored Target data (i.e., the target images uploaded by developers).
- Result return: After a successful match, the cloud returns the recognition results (target ID, target image, etc.) to the client, which then displays virtual content or uses the image for subsequent tracking.
Difference from planar image tracking
| Feature | Planar image tracking (local) | Cloud recognition (cloud) |
|---|---|---|
| Recognition computation | Done locally on the device | Done on the cloud server |
| Target library size | Limited by memory and loading time trade-offs, typically not recommended to exceed 100 images | Single library supports up to 100,000 target images, expandable to hundreds of millions |
| Target updates | Requires repackaging and redistributing the app | Real-time upload, takes effect immediately |
| Network dependency | No network required (works offline) | Requires internet (recognition requests need network) |
| Functional focus | Recognition and continuous tracking (outputs 6DoF pose) | One-time recognition (target matching) |
Key notes:
- Recognition: Only matches "what this target is" without continuous tracking. For tracking, local planar image tracking must be combined.
- Use cases: Large number of targets (e.g., product catalogs, children's picture books), frequent updates (e.g., event posters), or single functional requirements (e.g., recognition-only without tracking).
Service usage and management
EasyAR CRS provides a flexible and secure cloud target management solution, supporting diverse needs from individual development to enterprise-level applications.
Gallery isolation and security
- Multiple gallery support: You can create multiple independent CRS galleries, each completely isolated with no target conflicts. For example:
- Gallery A: for marketing campaigns, storing product posters.
- Gallery B: for education and training, storing textbook illustrations.
- Security mechanism: Each gallery is accessed through a unique API key and secret to ensure data security.
Concurrency mode selection
Based on application scale and scanning volume requirements, CRS offers two concurrency modes:
| Mode | Applicable scenarios | Features | Activation method |
|---|---|---|---|
| Basic concurrency | AR application QPS < 50, general scanning volume | Self-service activation, stable and reliable | Apply online in the CRS console |
| High concurrency | AR application QPS ≥ 50, high traffic scanning | Dedicated resource guarantee, low latency | Contact EasyAR technical support for evaluation and activation |
Tip
For startup projects or testing phases, the basic mode can be selected. After the application goes live, decide whether to upgrade based on actual traffic (e.g., monitoring QPS to identify request volume).
Gallery management and API
- Gallery management: For daily operations (such as creating, deleting, uploading targets), please refer to the gallery management section, which contains detailed steps and screenshots.
- CRS API: Provides a rich REST API, supporting the following scenarios:
- Health check: Query service status via API.
- Automation: Batch upload, delete, modify, and query targets.
- Utility tools: Target recognition scoring, similarity conflict checking.
Note
CRS supports integration via SDK, WeChat Mini Program, Web, etc. SDK integration is only supported for EasyAR Sense v2.0.0 and above.
Effects and expected results
Understanding the actual performance of cloud recognition helps developers set reasonable project goals. The following describes the effects in typical scenarios:
Ideal effect
- Fast recognition speed: From shooting to returning results, the delay is <1 second (when the network is good).
- High recognition accuracy: When the target image is clear and the network is stable, the accuracy rate is >98%.
- Supports large-scale target library: A single library can manage up to 100,000 target recognition images.
- Real-time updates: After uploading a new target, the client can recognize it without updating (only requires an internet connection).
Non-ideal situations and countermeasures
| Phenomenon | Cause | User perception | Solution |
|---|---|---|---|
| High recognition latency | Poor network, slow image upload | Need to wait several seconds for results | Provide appropriate prompts in the app |
| Recognition failure | Blurry image, target not uploaded to the cloud | Virtual content does not appear | Check CRS target library status, guide users to stabilize the device |
| Target conflict | Too many similar images in the target library | Incorrect target recognized | Optimize target images to increase distinctiveness, or manage similar images in separate libraries |
Expected result verification method
- Development phase: Upload test targets on the EasyAR CRS console, first verify the recognition process and familiarize yourself with the application logic through the HelloARCRS sample, then integrate it into your own application.
- Testing phase: Use your own application to test recognition success rates under various conditions, such as weak network environments, dynamically updating target images, and increasing the size of the cloud image library.
Best practices
Cloud recognition extends the target library capacity and dynamic update capabilities through cloud computing but sacrifices offline functionality and real-time tracking. Developers should choose a solution based on project requirements (target quantity, update frequency, network environment, etc.): use local tracking for small-scale static scenarios and cloud recognition for large-scale dynamic scenarios.
When using CRS, it is recommended to follow this development pattern:
- Testing phase: Use the basic concurrency mode and upload a small number of targets to verify the workflow.
- Before launch: Estimate the expected concurrency and contact technical support in advance to upgrade to high-concurrency mode (requires 1–2 business days).
- Operation phase: Regularly monitor the gallery health status using the API to ensure service stability.
Important
Seasonal traffic peak warning: If your application may face temporary surges in concurrency during holidays, major events, or marketing campaigns, be sure to contact EasyAR technical support at least 3 business days in advance to request a service upgrade and avoid restricted recognition service usage.
Additionally, if your application requires a large-scale gallery while also needing target image tracking, you can combine cloud recognition with the planar image tracking feature. For specific details and instructions, refer to the Combining with planar image tracking section.