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