Detections Stabilizer

Class: StabilizeTrackedDetectionsBlockV1

Source: inference.core.workflows.core_steps.transformations.stabilize_detections.v1.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: VLM as Detector, Byte Tracker, Google Vision OCR, Overlap Filter, SAM 3, Classification Label Visualization, Detections Stabilizer, Circle Visualization, SIFT Comparison, Image Contours, Relative Static Crop, Detections Filter, Image Preprocessing, Ellipse Visualization, Stitch Images, Triangle Visualization, Stability AI Inpainting, Detections Combine, QR Code Generator, Image Slicer, Background Color Visualization, Segment Anything 2 Model, Template Matching, Moondream2, Velocity, OCR Model, Distance Measurement, Dot Visualization, SIFT, Morphological Transformation, Detections Transformation, EasyOCR, Reference Path Visualization, Halo Visualization, SIFT Comparison, Polygon Visualization, Image Slicer, Detection Offset, Clip Comparison, Image Convert Grayscale, Instance Segmentation Model, Byte Tracker, Color Visualization, Line Counter, PTZ Tracking (ONVIF).md), Object Detection Model, Label Visualization, Trace Visualization, Byte Tracker, Dynamic Zone, Line Counter, YOLO-World Model, Corner Visualization, Mask Visualization, Time in Zone, Stability AI Outpainting, Detections Stitch, Blur Visualization, Time in Zone, Crop Visualization, VLM as Detector, Grid Visualization, Detections Classes Replacement, Perspective Correction, Absolute Static Crop, Seg Preview, Contrast Equalization, Polygon Zone Visualization, Stability AI Image Generation, Depth Estimation, Bounding Box Visualization, Camera Focus, Line Counter Visualization, Instance Segmentation Model, Icon Visualization, Image Blur, Time in Zone, Pixelate Visualization, Image Threshold, Detections Merge, Path Deviation, Identify Outliers, Pixel Color Count, Dynamic Crop, Bounding Rectangle, Path Deviation, Detections Consensus, Model Comparison Visualization, Camera Calibration, Keypoint Visualization, Identify Changes, Object Detection Model
• outputs: Byte Tracker, Overlap Filter, Blur Visualization, Detections Stabilizer, Circle Visualization, Time in Zone, Crop Visualization, Detections Filter, Detections Classes Replacement, Perspective Correction, Ellipse Visualization, Triangle Visualization, Roboflow Dataset Upload, Detections Combine, Stability AI Inpainting, Detections Stitch, Roboflow Dataset Upload, Background Color Visualization, Model Monitoring Inference Aggregator, Segment Anything 2 Model, Velocity, Distance Measurement, Dot Visualization, Florence-2 Model, Bounding Box Visualization, Detections Transformation, Halo Visualization, Icon Visualization, Polygon Visualization, Florence-2 Model, Time in Zone, Detection Offset, Pixelate Visualization, Path Deviation, Byte Tracker, PTZ Tracking (ONVIF).md), Color Visualization, Line Counter, Detections Merge, Label Visualization, Byte Tracker, Trace Visualization, Dynamic Zone, Dynamic Crop, Bounding Rectangle, Path Deviation, Line Counter, Detections Consensus, Model Comparison Visualization, Size Measurement, Corner Visualization, Mask Visualization, Time in Zone, Roboflow Custom Metadata, Stitch OCR Detections

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[object_detection_prediction, instance_segmentation_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 if object_detection_prediction or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object if instance_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
}