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