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

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[object_detection_prediction, instance_segmentation_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
}