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