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:
Segment Anything 2 Model,Image Slicer,Detections Filter,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Clip Comparison,Object Detection Model,Path Deviation,Relative Static Crop,Detections Consensus,Object Detection Model,SIFT Comparison,Detection Offset,Grid Visualization,Ellipse Visualization,SIFT,Model Comparison Visualization,VLM as Detector,Halo Visualization,Image Contours,Crop Visualization,Absolute Static Crop,Byte Tracker,Camera Focus,Image Blur,Trace Visualization,Distance Measurement,Circle Visualization,Velocity,Image Preprocessing,VLM as Detector,Background Color Visualization,Dot Visualization,Google Vision OCR,Bounding Rectangle,Identify Changes,Polygon Zone Visualization,Pixel Color Count,Identify Outliers,Classification Label Visualization,Bounding Box Visualization,Corner Visualization,Byte Tracker,Image Slicer,Byte Tracker,Dynamic Crop,Reference Path Visualization,Line Counter,Label Visualization,Detections Stabilizer,Mask Visualization,Stitch Images,Triangle Visualization,Stability AI Image Generation,Image Threshold,Line Counter Visualization,Template Matching,Dynamic Zone,Detections Transformation,Detections Stitch,Keypoint Visualization,Time in Zone,Color Visualization,Path Deviation,Blur Visualization,YOLO-World Model,Line Counter,Instance Segmentation Model,Time in Zone,SIFT Comparison,Image Convert Grayscale,Instance Segmentation Model,Detections Classes Replacement,Polygon Visualization - outputs:
Segment Anything 2 Model,Detections Filter,Stitch OCR Detections,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Path Deviation,Roboflow Custom Metadata,Detections Consensus,Detection Offset,Roboflow Dataset Upload,Ellipse Visualization,Model Comparison Visualization,Halo Visualization,Crop Visualization,Byte Tracker,Trace Visualization,Distance Measurement,Circle Visualization,Velocity,Dot Visualization,Background Color Visualization,Bounding Rectangle,Roboflow Dataset Upload,Size Measurement,Florence-2 Model,Byte Tracker,Corner Visualization,Bounding Box Visualization,Florence-2 Model,Byte Tracker,Dynamic Crop,Line Counter,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Dynamic Zone,Detections Transformation,Detections Stitch,Model Monitoring Inference Aggregator,Time in Zone,Color Visualization,Path Deviation,Blur Visualization,Line Counter,Time in Zone,Detections Classes Replacement,Polygon 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
}