Background Subtraction

Class: BackgroundSubtractionBlockV1

Source: inference.core.workflows.core_steps.classical_cv.background_subtraction.v1.BackgroundSubtractionBlockV1

This block uses background subtraction to detect motion in an image in order to highlight areas of motion. The output of the block can be used to train and infer on motion based models.

Type identifier

Use the following identifier in step "type" field: roboflow_core/background_subtraction@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
threshold	int	The threshold value for the squared Mahalanobis distance for background subtraction. Smaller values increase sensitivity to motion. Recommended values are 8-32..	✅
history	int	The number of previous frames to use for background subtraction. Larger values make the model less sensitive to quick changes in the background, smaller values allow for more adaptation..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Background Subtraction in version v1.

• inputs: Keypoint Visualization, Blur Visualization, Camera Focus, Background Subtraction, Dot Visualization, Line Counter Visualization, Pixel Color Count, Line Counter, Crop Visualization, Stitch Images, Dynamic Crop, Circle Visualization, Template Matching, Camera Calibration, Absolute Static Crop, Mask Visualization, Ellipse Visualization, Image Contours, Color Visualization, Image Preprocessing, Halo Visualization, Model Comparison Visualization, SIFT Comparison, Distance Measurement, Line Counter, SIFT Comparison, Trace Visualization, Depth Estimation, Perspective Correction, Grid Visualization, Stability AI Outpainting, Contrast Equalization, Morphological Transformation, QR Code Generator, Polygon Zone Visualization, Reference Path Visualization, Stability AI Image Generation, Stability AI Inpainting, Bounding Box Visualization, Image Slicer, Corner Visualization, Polygon Visualization, Image Blur, Image Threshold, Relative Static Crop, Image Slicer, Pixelate Visualization, Triangle Visualization, Label Visualization, Image Convert Grayscale, SIFT, Icon Visualization, Background Color Visualization, Classification Label Visualization
• outputs: Moondream2, Line Counter Visualization, Blur Visualization, Detections Stitch, LMM, Instance Segmentation Model, Multi-Label Classification Model, Time in Zone, Instance Segmentation Model, Dominant Color, Multi-Label Classification Model, Dynamic Crop, Circle Visualization, Camera Calibration, Absolute Static Crop, Mask Visualization, Single-Label Classification Model, Google Gemini, Ellipse Visualization, Anthropic Claude, Email Notification, Color Visualization, Keypoint Detection Model, Image Preprocessing, EasyOCR, OCR Model, QR Code Detection, Llama 3.2 Vision, OpenAI, OpenAI, SAM 3, SAM 3, Florence-2 Model, Trace Visualization, Depth Estimation, Perspective Correction, Perception Encoder Embedding Model, VLM as Detector, Stability AI Outpainting, Contrast Equalization, Google Gemini, Polygon Zone Visualization, Reference Path Visualization, Roboflow Dataset Upload, Stability AI Inpainting, Bounding Box Visualization, Image Slicer, Corner Visualization, Polygon Visualization, CogVLM, Relative Static Crop, Triangle Visualization, Pixelate Visualization, Icon Visualization, Background Color Visualization, Classification Label Visualization, Keypoint Visualization, Camera Focus, Background Subtraction, Dot Visualization, Pixel Color Count, Object Detection Model, Crop Visualization, Stitch Images, Object Detection Model, Qwen2.5-VL, Florence-2 Model, LMM For Classification, VLM as Detector, Template Matching, Image Contours, Single-Label Classification Model, Keypoint Detection Model, OpenAI, Barcode Detection, Roboflow Dataset Upload, YOLO-World Model, SAM 3, Halo Visualization, Model Comparison Visualization, CLIP Embedding Model, SIFT Comparison, Motion Detection, Byte Tracker, VLM as Classifier, Clip Comparison, Clip Comparison, Seg Preview, VLM as Classifier, Morphological Transformation, Segment Anything 2 Model, Google Vision OCR, Buffer, Stability AI Image Generation, Detections Stabilizer, Image Blur, Image Threshold, Image Slicer, OpenAI, SmolVLM2, Anthropic Claude, Label Visualization, Image Convert Grayscale, SIFT, Gaze Detection

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Background Subtraction in version v1 has.

Bindings

• input

  • image (image): The input image for this step..
  • threshold (integer): The threshold value for the squared Mahalanobis distance for background subtraction. Smaller values increase sensitivity to motion. Recommended values are 8-32..
  • history (integer): The number of previous frames to use for background subtraction. Larger values make the model less sensitive to quick changes in the background, smaller values allow for more adaptation..

• output

  • image (image): Image in workflows.

Example JSON definition of step Background Subtraction in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/background_subtraction@v1",
    "image": "$inputs.image",
    "threshold": 16,
    "history": 30
}