Detect and Track Objects with ML Kit on iOS

This page describes an old version of the Object Detection and Tracking API, which was part of ML Kit for Firebase. Development of this API has been moved to the standalone ML Kit SDK, which you can use with or without Firebase. Learn more.

See Detect and track objects with ML Kit on iOS for the latest documentation.

You can use ML Kit to detect and track objects across frames of video.

When you pass ML Kit images, ML Kit returns, for each image, a list of up to five detected objects and their position in the image. When detecting objects in video streams, every object has an ID that you can use to track the object across images. You can also optionally enable coarse object classification, which labels objects with broad category descriptions.

Before you begin

1. If you have not already added Firebase to your app, do so by following the steps in the getting started guide.
2. Include the ML Kit libraries in your Podfile:

   pod 'Firebase/MLVision', '6.25.0'
   pod 'Firebase/MLVisionObjectDetection', '6.25.0'
   

   After you install or update your project's Pods, be sure to open your Xcode project using its .xcworkspace.
3. In your app, import Firebase:

   Swift

   import Firebase

   Objective-C

   @import Firebase;

Note: ML Kit's object detection and tracking API only runs on 64-bit devices. If you build your app with 32-bit support, check the device's architecture before using this API.

1. Configure the object detector

To start detecting and tracking objects, first create an instance of VisionObjectDetector, optionally specifying any detector settings you want to change from the default.

1. Configure the object detector for your use case with a VisionObjectDetectorOptions object. You can change the following settings:

   Object Detector Settings
   Detection mode	.stream (default) | .singleImage In stream mode (default), the object detector runs with very low latency, but might produce incomplete results (such as unspecified bounding boxes or category) on the first few invocations of the detector. Also, in stream mode, the detector assigns tracking IDs to objects, which you can use to track objects across frames. Use this mode when you want to track objects, or when low latency is important, such as when processing video streams in real time. In single image mode, the object detector waits until a detected object's bounding box and (if you enabled classification) category are available before returning a result. As a consequence, detection latency is potentially higher. Also, in single image mode, tracking IDs are not assigned. Use this mode if latency isn't critical and you don't want to deal with partial results.
   Detect and track multiple objects	false (default) | true Whether to detect and track up to five objects or only the most prominent object (default).
   Classify objects	false (default) | true Whether or not to classify detected objects into coarse categories. When enabled, the object detector classifies objects into the following categories: fashion goods, food, home goods, places, plants, and unknown.

   The object detection and tracking API is optimized for these two core use cases:

   • Live detection and tracking of the most prominent object in the camera viewfinder
   • Detection of multiple objects in a static image

   To configure the API for these use cases:

   Swift

   // Live detection and tracking
   let options = VisionObjectDetectorOptions()
   options.detectorMode = .stream
   options.shouldEnableMultipleObjects = false
   options.shouldEnableClassification = true  // Optional
   
   // Multiple object detection in static images
   let options = VisionObjectDetectorOptions()
   options.detectorMode = .singleImage
   options.shouldEnableMultipleObjects = true
   options.shouldEnableClassification = true  // Optional
   

   Objective-C

   // Live detection and tracking
   FIRVisionObjectDetectorOptions *options = [[FIRVisionObjectDetectorOptions alloc] init];
   options.detectorMode = FIRVisionObjectDetectorModeStream;
   options.shouldEnableMultipleObjects = NO;
   options.shouldEnableClassification = YES;  // Optional
   
   // Multiple object detection in static images
   FIRVisionObjectDetectorOptions *options = [[FIRVisionObjectDetectorOptions alloc] init];
   options.detectorMode = FIRVisionObjectDetectorModeSingleImage;
   options.shouldEnableMultipleObjects = YES;
   options.shouldEnableClassification = YES;  // Optional
   

2. Get an instance of FirebaseVisionObjectDetector:

   Swift

   let objectDetector = Vision.vision().objectDetector()
   
   // Or, to change the default settings:
   let objectDetector = Vision.vision().objectDetector(options: options)
   

   Objective-C

   FIRVisionObjectDetector *objectDetector = [[FIRVision vision] objectDetector];
   
   // Or, to change the default settings:
   FIRVisionObjectDetector *objectDetector = [[FIRVision vision] objectDetectorWithOptions:options];
   

2. Run the object detector

To detect and track objects, do the following for each image or frame of video. If you enabled stream mode, you must create VisionImage objects from CMSampleBufferRefs.

1. Create a VisionImage object using a UIImage or a CMSampleBufferRef.

   To use a UIImage:

   1. If necessary, rotate the image so that its imageOrientation property is .up.
   2. Create a VisionImage object using the correctly-rotated UIImage. Do not specify any rotation metadata—the default value, .topLeft, must be used.

      Swift

      let image = VisionImage(image: uiImage)

      Objective-C

      FIRVisionImage *image = [[FIRVisionImage alloc] initWithImage:uiImage];

   To use a CMSampleBufferRef:

   1. Create a VisionImageMetadata object that specifies the orientation of the image data contained in the CMSampleBufferRef buffer.

      To get the image orientation:

      Swift

      func imageOrientation(
          deviceOrientation: UIDeviceOrientation,
          cameraPosition: AVCaptureDevice.Position
          ) -> VisionDetectorImageOrientation {
          switch deviceOrientation {
          case .portrait:
              return cameraPosition == .front ? .leftTop : .rightTop
          case .landscapeLeft:
              return cameraPosition == .front ? .bottomLeft : .topLeft
          case .portraitUpsideDown:
              return cameraPosition == .front ? .rightBottom : .leftBottom
          case .landscapeRight:
              return cameraPosition == .front ? .topRight : .bottomRight
          case .faceDown, .faceUp, .unknown:
              return .leftTop
          }
      }

      Objective-C

      - (FIRVisionDetectorImageOrientation)
          imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation
                                 cameraPosition:(AVCaptureDevicePosition)cameraPosition {
        switch (deviceOrientation) {
          case UIDeviceOrientationPortrait:
            if (cameraPosition == AVCaptureDevicePositionFront) {
              return FIRVisionDetectorImageOrientationLeftTop;
            } else {
              return FIRVisionDetectorImageOrientationRightTop;
            }
          case UIDeviceOrientationLandscapeLeft:
            if (cameraPosition == AVCaptureDevicePositionFront) {
              return FIRVisionDetectorImageOrientationBottomLeft;
            } else {
              return FIRVisionDetectorImageOrientationTopLeft;
            }
          case UIDeviceOrientationPortraitUpsideDown:
            if (cameraPosition == AVCaptureDevicePositionFront) {
              return FIRVisionDetectorImageOrientationRightBottom;
            } else {
              return FIRVisionDetectorImageOrientationLeftBottom;
            }
          case UIDeviceOrientationLandscapeRight:
            if (cameraPosition == AVCaptureDevicePositionFront) {
              return FIRVisionDetectorImageOrientationTopRight;
            } else {
              return FIRVisionDetectorImageOrientationBottomRight;
            }
          default:
            return FIRVisionDetectorImageOrientationTopLeft;
        }
      }

      Then, create the metadata object:

      Swift

      let cameraPosition = AVCaptureDevice.Position.back  // Set to the capture device you used.
      let metadata = VisionImageMetadata()
      metadata.orientation = imageOrientation(
          deviceOrientation: UIDevice.current.orientation,
          cameraPosition: cameraPosition
      )

      Objective-C

      FIRVisionImageMetadata *metadata = [[FIRVisionImageMetadata alloc] init];
      AVCaptureDevicePosition cameraPosition =
          AVCaptureDevicePositionBack;  // Set to the capture device you used.
      metadata.orientation =
          [self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation
                                       cameraPosition:cameraPosition];

   2. Create a VisionImage object using the CMSampleBufferRef object and the rotation metadata:

      Swift

      let image = VisionImage(buffer: sampleBuffer)
      image.metadata = metadata

      Objective-C

      FIRVisionImage *image = [[FIRVisionImage alloc] initWithBuffer:sampleBuffer];
      image.metadata = metadata;

2. Pass the VisionImage to one of the object detector's image processing methods. You can either use the asynchronous process(image:) method or the synchronous results() method.

   To detect objects asynchronously:

   Swift

   objectDetector.process(image) { detectedObjects, error in
     guard error == nil else {
       // Error.
       return
     }
     guard let detectedObjects = detectedObjects, !detectedObjects.isEmpty else {
       // No objects detected.
       return
     }
   
     // Success. Get object info here.
     // ...
   }
   

   Objective-C

   [objectDetector processImage:image
                     completion:^(NSArray<FIRVisionObject *> * _Nullable objects,
                                  NSError * _Nullable error) {
                       if (error == nil) {
                         return;
                       }
                       if (objects == nil | objects.count == 0) {
                         // No objects detected.
                         return;
                       }
   
                       // Success. Get object info here.
                       // ...
                     }];
   

   To detect objects synchronously:

   Swift

   var results: [VisionObject]? = nil
   do {
     results = try objectDetector.results(in: image)
   } catch let error {
     print("Failed to detect object with error: \(error.localizedDescription).")
     return
   }
   guard let detectedObjects = results, !detectedObjects.isEmpty else {
     print("Object detector returned no results.")
     return
   }
   
   // ...
   

   Objective-C

   NSError *error;
   NSArray<FIRVisionObject *> *objects = [objectDetector resultsInImage:image
                                                                  error:&error];
   if (error == nil) {
     return;
   }
   if (objects == nil | objects.count == 0) {
     // No objects detected.
     return;
   }
   
   // Success. Get object info here.
   // ...
   

3. If the call to the image processor succeeds, it either passes a list of VisionObjects to the completion handler or returns the list, depending on whether you called the asynchronous or synchronous method.

   Note: In streaming mode, the object detector might need to process 30 or more frames, depending on device performance, before it detects the first object.

   Each VisionObject contains the following properties:

   frame	A CGRect indicating the position of the object in the image.
   trackingID	An integer that identifies the object across images. Nil in single image mode.
   classificationCategory	The coarse category of the object. If the object detector doesn't have classification enabled, this is always .unknown.
   confidence	The confidence value of the object classification. If the object detector doesn't have classification enabled, or the object is classified as unknown, this is nil.

   Swift

   // detectedObjects contains one item if multiple object detection wasn't enabled.
   for obj in detectedObjects {
     let bounds = obj.frame
     let id = obj.trackingID
   
     // If classification was enabled:
     let category = obj.classificationCategory
     let confidence = obj.confidence
   }
   

   Objective-C

   // The list of detected objects contains one item if multiple
   // object detection wasn't enabled.
   for (FIRVisionObject *obj in objects) {
     CGRect bounds = obj.frame;
     if (obj.trackingID) {
       NSInteger id = obj.trackingID.integerValue;
     }
   
     // If classification was enabled:
     FIRVisionObjectCategory category = obj.classificationCategory;
     float confidence = obj.confidence.floatValue;
   }
   

Improving usability and performance

For the best user experience, follow these guidelines in your app:

• Successful object detection depends on the object's visual complexity. Objects with a small number of visual features might need to take up a larger part of the image to be detected. You should provide users with guidance on capturing input that works well with the kind of objects you want to detect.
• When using classification, if you want to detect objects that don't fall cleanly into the supported categories, implement special handling for unknown objects.

Also, check out the [ML Kit Material Design showcase app][showcase-link]{: .external } and the Material Design Patterns for machine learning-powered features collection.

When using streaming mode in a real-time application, follow these guidelines to achieve the best framerates:

• Don't use multiple object detection in streaming mode, as most devices won't be able to produce adequate framerates.

• Disable classification if you don't need it.

• Throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame.
• If you are using the output of the detector to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each input frame. See the previewOverlayView and FIRDetectionOverlayView classes in the showcase sample app for an example.