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

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
}