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

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
}