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

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
}