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