Velocity

Class: VelocityBlockV1

Source: inference.core.workflows.core_steps.analytics.velocity.v1.VelocityBlockV1

The VelocityBlock computes the velocity and speed of objects tracked across video frames. It includes options to smooth the velocity and speed measurements over time and to convert units from pixels per second to meters per second. It requires detections from Byte Track with unique tracker_id assigned to each object, which persists between frames. The velocities are calculated based on the displacement of object centers over time.

Note: due to perspective and camera distortions calculated velocity will be different depending on object position in relation to the camera.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
smoothing_alpha	float	Smoothing factor (alpha) for exponential moving average (0 < alpha <= 1). Lower alpha means more smoothing..	✅
pixels_per_meter	float	Conversion from pixels to meters. Velocity will be converted to meters per second using this value..	✅

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 Velocity in version v1.

• inputs: Path Deviation, Dynamic Zone, Detections Transformation, Template Matching, Detections Classes Replacement, VLM as Detector, Object Detection Model, Instance Segmentation Model, Line Counter, Byte Tracker, Time in Zone, Detections Stitch, Object Detection Model, Segment Anything 2 Model, YOLO-World Model, Detections Merge, Camera Focus, Gaze Detection, Moondream2, OCR Model, Seg Preview, Byte Tracker, Path Deviation, Time in Zone, Identify Changes, Instance Segmentation Model, Detections Filter, Overlap Filter, Detection Offset, Bounding Rectangle, Google Vision OCR, EasyOCR, Detections Stabilizer, Time in Zone, Detections Combine, SAM 3, VLM as Detector, Dynamic Crop, Velocity, Cosine Similarity, Detections Consensus, Byte Tracker, PTZ Tracking (ONVIF).md), Perspective Correction
• outputs: Polygon Visualization, Roboflow Dataset Upload, Background Color Visualization, Size Measurement, Dynamic Zone, Path Deviation, Detections Transformation, Corner Visualization, Model Monitoring Inference Aggregator, Detections Classes Replacement, Distance Measurement, Trace Visualization, Mask Visualization, Line Counter, Bounding Box Visualization, Model Comparison Visualization, Byte Tracker, Pixelate Visualization, Time in Zone, Florence-2 Model, Detections Stitch, Ellipse Visualization, Triangle Visualization, Segment Anything 2 Model, Crop Visualization, Detections Merge, Icon Visualization, Byte Tracker, Color Visualization, Roboflow Dataset Upload, Path Deviation, Label Visualization, Time in Zone, Roboflow Custom Metadata, Florence-2 Model, Blur Visualization, Dot Visualization, Overlap Filter, Detections Filter, Circle Visualization, Detection Offset, Bounding Rectangle, Line Counter, Detections Stabilizer, Detections Combine, Time in Zone, Dynamic Crop, Velocity, Detections Consensus, Stitch OCR Detections, Byte Tracker, PTZ Tracking (ONVIF).md), Halo Visualization, Stability AI Inpainting, Perspective Correction

Input and Output Bindings

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

Bindings

• input

  • image (image): not available.
  • detections (Union[instance_segmentation_prediction, object_detection_prediction]): Model predictions to calculate the velocity for..
  • smoothing_alpha (float): Smoothing factor (alpha) for exponential moving average (0 < alpha <= 1). Lower alpha means more smoothing..
  • pixels_per_meter (float): Conversion from pixels to meters. Velocity will be converted to meters per second using this value..

• output

  • velocity_detections (Union[object_detection_prediction, instance_segmentation_prediction]): Prediction with detected bounding boxes in form of sv.Detections(...) object if object_detection_prediction or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object if instance_segmentation_prediction.

Example JSON definition of step Velocity in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/velocity@v1",
    "image": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "smoothing_alpha": 0.5,
    "pixels_per_meter": 0.01
}