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