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

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
}