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

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
}