Detections Stabilizer¶
Class: StabilizeTrackedDetectionsBlockV1
This block stores last known position for each bounding box If box disappears then this block will bring it back so short gaps are filled with last known box position The block requires detections to be tracked (i.e. each object must have unique tracker_id assigned, which persists between frames) WARNING: this block will produce many short-lived bounding boxes for unstable trackers!
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/stabilize_detections@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_window_size |
int |
Predicted movement of detection will be smoothed based on historical measurements of velocity, this parameter controls number of historical measurements taken under account when calculating smoothed velocity. Detections will be removed from generating smoothed predictions if they had been missing for longer than this number of frames.. | ✅ |
bbox_smoothing_coefficient |
float |
Bounding box smoothing coefficient applied when given tracker_id is present on current frame. This parameter must be initialized with value between 0 and 1. | ✅ |
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 Detections Stabilizer in version v1.
- inputs:
Circle Visualization,Background Color Visualization,Corner Visualization,VLM as Detector,Polygon Zone Visualization,Camera Focus,Image Slicer,Line Counter,Image Blur,Dot Visualization,Path Deviation,Detections Merge,Detection Offset,Stability AI Inpainting,Pixelate Visualization,Line Counter,Detections Consensus,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Detections Classes Replacement,Dynamic Zone,Instance Segmentation Model,Camera Calibration,Object Detection Model,Classification Label Visualization,Google Vision OCR,Byte Tracker,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Object Detection Model,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Image Slicer,Detections Transformation,Crop Visualization,Detections Stitch,Identify Outliers,YOLO-World Model,Relative Static Crop,Model Comparison Visualization,Perspective Correction,Byte Tracker,Path Deviation,Mask Visualization,Time in Zone,Detections Filter,Clip Comparison,Time in Zone,Dynamic Crop,Template Matching,Byte Tracker,Instance Segmentation Model,Image Contours,SIFT,SIFT Comparison,Reference Path Visualization,Triangle Visualization,Bounding Rectangle,Velocity,SIFT Comparison,VLM as Detector,Keypoint Visualization,Identify Changes,Grid Visualization,Segment Anything 2 Model,Detections Stabilizer,Stitch Images,Blur Visualization - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Trace Visualization,Label Visualization,Detections Transformation,Crop Visualization,Detections Stitch,Path Deviation,Detections Merge,Dot Visualization,Detection Offset,Roboflow Dataset Upload,Model Comparison Visualization,Stability AI Inpainting,Pixelate Visualization,Line Counter,Stitch OCR Detections,Perspective Correction,Detections Consensus,Byte Tracker,Path Deviation,Detections Stabilizer,Distance Measurement,Mask Visualization,Time in Zone,Detections Filter,Color Visualization,Time in Zone,Dynamic Crop,Halo Visualization,Polygon Visualization,Byte Tracker,Detections Classes Replacement,Florence-2 Model,Dynamic Zone,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,Roboflow Dataset Upload,Roboflow Custom Metadata,Byte Tracker,Line Counter,Segment Anything 2 Model,Ellipse Visualization,Size Measurement,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Detections Stabilizer in version v1 has.
Bindings
-
input
image(image): not available.detections(Union[instance_segmentation_prediction,object_detection_prediction]): Tracked detections.smoothing_window_size(integer): Predicted movement of detection will be smoothed based on historical measurements of velocity, this parameter controls number of historical measurements taken under account when calculating smoothed velocity. Detections will be removed from generating smoothed predictions if they had been missing for longer than this number of frames..bbox_smoothing_coefficient(float_zero_to_one): Bounding box smoothing coefficient applied when given tracker_id is present on current frame. This parameter must be initialized with value between 0 and 1.
-
output
tracked_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 Detections Stabilizer in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/stabilize_detections@v1",
"image": "<block_does_not_provide_example>",
"detections": "$steps.object_detection_model.predictions",
"smoothing_window_size": 5,
"bbox_smoothing_coefficient": 0.2
}