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