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

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
}