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Machine Vision

Detect Objects in Android AR: A Guide

By Jan on 03/09/2025

Learn how to use Android's ARCore technology to detect and interact with physical objects in your augmented reality applications.

Detect Objects in Android AR: A Guide

Table of Contents

Introduction

Augmented reality (AR) object recognition allows your apps to understand and interact with the real world through the lens of a device camera. This article provides a step-by-step guide on how to implement object recognition in your AR applications. We'll cover key concepts, popular frameworks, and provide code examples to get you started.

Step-by-Step Guide

  1. Choose your AR framework: Popular options include ARCore (Android), ARKit (iOS), and AR Foundation (Unity).

    # Example using ARFoundation in Unity
using UnityEngine.XR.ARFoundation;

  2. Access device camera: Your framework will provide ways to access the camera feed.

    # Example using ARFoundation
ARSession.Start();

  3. Implement object recognition:

    • Pre-trained models: Use frameworks like Google ML Kit or Apple's Vision framework for common objects.
    • Custom models: Train your own model using tools like TensorFlow Lite or Create ML.

  4. Integrate with AR scene:

    • Get object's position: Use the framework's APIs to get the detected object's position in the real world.
    • Anchor virtual content: Place virtual objects relative to the detected object.
    # Example using ARFoundation
var anchor = arSessionOrigin.trackablesParent.AddAnchor(pose);

  5. Handle object tracking: Update the virtual content's position as the detected object moves.

    // Example using ARFoundation
void Update() {
    if (anchor != null) {
        // Update virtual object's position based on anchor's pose
    }
}

  6. Consider object occlusion: Make virtual objects appear behind real-world objects for a more realistic experience.

    // Example using ARFoundation
arCameraBackground.useDepthOcclusion = true;

Code Example

This code example shows how to use ARFoundation in Unity to recognize and track an image. It guides you through setting up a new Unity project, adding necessary AR packages, and configuring the scene with an AR Session Origin and an AR Camera. You'll learn how to create an image library containing your reference image and use a C# script to detect and track this image in the real world. The script handles events related to tracked images, such as adding, updating, and removing them. When the image is detected, a predefined 3D prefab is instantiated at the image's location, and its position and rotation are continuously tracked. This provides a basic framework for building image recognition and tracking features in AR applications.

This example demonstrates how to recognize and track an image using ARFoundation in Unity.

1. Project Setup:

  • Create a new Unity project.
  • Add the AR Foundation and ARCore XR Plugin packages (or ARKit XR Plugin for iOS) through the Package Manager.

2. Scene Setup:

  • Create a new scene.
  • Delete the default "Main Camera" object.
  • Add an "AR Session Origin" object from the Hierarchy window.
  • Add an "AR Session" object as a child of the "AR Session Origin".
  • Add an "AR Camera Background" component to the "AR Camera" object (child of "AR Session Origin").

3. Image Recognition Setup:

  • Create a new folder named "Resources/Images" in your project window.
  • Add your reference image to this folder (e.g., "referenceImage.jpg").
  • Create a new "XR -> Reference Image Library" asset and name it "MyImageLibrary".
  • Add your reference image to the "MyImageLibrary" asset.
  • Create a new script named "ImageRecognition.cs".
  • Attach the "ImageRecognition.cs" script to the "AR Session Origin" object.

4. ImageRecognition.cs Script:

using System.Collections.Generic;
using UnityEngine;
using UnityEngine.XR.ARFoundation;
using UnityEngine.XR.ARSubsystems;

public class ImageRecognition : MonoBehaviour
{
    // Reference to the AR tracked image manager
    [SerializeField] private ARTrackedImageManager _trackedImageManager;

    // Reference to the prefab to instantiate when the image is detected
    [SerializeField] private GameObject _prefabToInstantiate;

    // Dictionary to store detected images and their corresponding instantiated prefabs
    private readonly Dictionary<string, GameObject> _instantiatedPrefabs = new Dictionary<string, GameObject>();

    // Called when the script is enabled
    private void OnEnable()
    {
        // Subscribe to the tracked images changed event
        _trackedImageManager.trackedImagesChanged += OnTrackedImagesChanged;
    }

    // Called when the script is disabled
    private void OnDisable()
    {
        // Unsubscribe from the tracked images changed event
        _trackedImageManager.trackedImagesChanged -= OnTrackedImagesChanged;
    }

    // Called when the list of tracked images changes
    private void OnTrackedImagesChanged(ARTrackedImagesChangedEventArgs eventArgs)
    {
        // Loop through all added tracked images
        foreach (var addedImage in eventArgs.added)
        {
            // Instantiate the prefab and store it in the dictionary
            _instantiatedPrefabs[addedImage.name] = Instantiate(_prefabToInstantiate, addedImage.transform);
        }

        // Loop through all updated tracked images
        foreach (var updatedImage in eventArgs.updated)
        {
            // Update the position and rotation of the instantiated prefab
            _instantiatedPrefabs[updatedImage.name].transform.position = updatedImage.transform.position;
            _instantiatedPrefabs[updatedImage.name].transform.rotation = updatedImage.transform.rotation;
        }

        // Loop through all removed tracked images
        foreach (var removedImage in eventArgs.removed)
        {
            // Destroy the instantiated prefab and remove it from the dictionary
            Destroy(_instantiatedPrefabs[removedImage.name]);
            _instantiatedPrefabs.Remove(removedImage.name);
        }
    }
}

5. Final Steps:

  • Assign the "MyImageLibrary" asset to the "Tracked Image Manager" component of the "AR Session Origin" object.
  • Create a prefab of the 3D object you want to display when the image is recognized.
  • Assign the prefab to the "_prefabToInstantiate" field in the "ImageRecognition.cs" script.
  • Build and run the application on your AR-supported device.

This code will detect the reference image, instantiate the assigned prefab at its location, and track its position and rotation. You can further customize this code to implement more complex interactions and functionalities.

Additional Notes

General:

  • Performance: Object recognition can be computationally expensive, especially on mobile devices. Consider optimizing model size and complexity, and use efficient algorithms for tracking and rendering.
  • Lighting conditions: Lighting can significantly impact object recognition accuracy. Train your models with diverse lighting conditions or implement techniques to handle varying lighting in real-time.
  • User experience: Provide clear feedback to the user during the recognition and tracking process. Use visual cues to indicate when an object is detected, tracked, or lost.

Frameworks and Tools:

  • ARCore and ARKit: These frameworks provide basic image recognition capabilities for detecting pre-defined images.
  • Google ML Kit and Apple Vision: Offer more advanced object detection and classification features, including support for custom models.
  • TensorFlow Lite and Create ML: Allow you to train and deploy custom machine learning models for object recognition on mobile devices.
  • Unity AR Foundation: Provides a higher-level abstraction layer over ARCore and ARKit, simplifying AR development in Unity.

Advanced Concepts:

  • Object persistence: Store the position and orientation of recognized objects so they remain in the AR scene even when they are temporarily out of view.
  • Multi-object recognition and tracking: Detect and track multiple objects simultaneously, handling potential occlusions and interactions between them.
  • Scene understanding: Go beyond object recognition to understand the overall scene geometry and context, enabling more immersive and interactive AR experiences.

Example Use Cases:

  • Interactive product demos: Allow users to visualize products in their own environment and interact with virtual features.
  • Educational apps: Create engaging learning experiences by overlaying information and animations on real-world objects.
  • Gaming: Develop AR games that blend virtual objects and characters with the real world.
  • Navigation and guidance: Provide real-time directions and instructions by overlaying them on the user's camera view.

Summary

This article outlines the key steps for building an augmented reality (AR) application capable of recognizing and interacting with real-world objects.

1. Framework Selection:

  • Choose an AR framework suited to your target platform:
    • ARCore: Android
    • ARKit: iOS
    • AR Foundation: Unity (cross-platform)

2. Camera Access:

  • Utilize your chosen framework's APIs to access the device's camera feed.

3. Object Recognition:

  • Pre-trained Models: Leverage existing models (e.g., Google ML Kit, Apple Vision) for recognizing common objects.
  • Custom Models: Train your own models using machine learning tools (e.g., TensorFlow Lite, Create ML) for specific object recognition needs.

4. AR Scene Integration:

  • Object Positioning: Obtain the detected object's real-world position using framework APIs.
  • Virtual Content Anchoring: Place virtual objects relative to the recognized object, creating an immersive AR experience.

5. Object Tracking:

  • Continuously update the virtual content's position based on the tracked object's movement, ensuring a seamless AR interaction.

6. Occlusion Handling:

  • Enhance realism by enabling occlusion, making virtual objects appear behind real-world objects for a more believable AR experience.

By following these steps, developers can create engaging AR applications that seamlessly blend virtual content with the real world, opening up a world of possibilities for interactive and immersive experiences.

Conclusion

From choosing the right framework to handling object occlusion for realism, each step contributes to a successful AR experience. As you delve deeper into AR development, you'll discover even more sophisticated techniques for object persistence, multi-object interactions, and scene understanding, pushing the boundaries of what's possible in blending the digital and physical worlds. The use cases are vast, spanning interactive product demos, immersive educational apps, engaging AR games, and real-time navigation guidance, all made possible by the power of AR object recognition.

References

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