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