Path Deviation

v2

Class: PathDeviationAnalyticsBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.path_deviation.v2.PathDeviationAnalyticsBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

The PathDeviationAnalyticsBlock is an analytics block designed to measure the Frechet distance of tracked objects from a user-defined reference path. The block requires detections to be tracked (i.e. each object must have a unique tracker_id assigned, which persists between frames).

Type identifier

Use the following identifier in step "type" field: roboflow_core/path_deviation_analytics@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
triggering_anchor	str	Triggering anchor. Allowed values: CENTER, CENTER_LEFT, CENTER_RIGHT, TOP_CENTER, TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_CENTER, BOTTOM_RIGHT, CENTER_OF_MASS.	✅
reference_path	List[Any]	Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].	✅

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 Path Deviation in version v2.

• inputs: Keypoint Detection Model, Line Counter, Google Vision OCR, Florence-2 Model, Template Matching, Model Monitoring Inference Aggregator, Florence-2 Model, Overlap Filter, CogVLM, OCR Model, Byte Tracker, Detections Transformation, VLM as Detector, Perspective Correction, Detections Stitch, CSV Formatter, OpenAI, Byte Tracker, Clip Comparison, Dynamic Zone, Multi-Label Classification Model, Object Detection Model, Time in Zone, Path Deviation, Slack Notification, Clip Comparison, Dimension Collapse, Byte Tracker, Detection Offset, Detections Consensus, Detections Stabilizer, Velocity, YOLO-World Model, OpenAI, Llama 3.2 Vision, Bounding Rectangle, Detections Filter, Anthropic Claude, Size Measurement, Time in Zone, Detections Merge, Moondream2, Segment Anything 2 Model, Webhook Sink, Roboflow Dataset Upload, Roboflow Custom Metadata, Single-Label Classification Model, Buffer, Path Deviation, VLM as Classifier, Local File Sink, Twilio SMS Notification, Stitch OCR Detections, PTZ Tracking (ONVIF).md), Dynamic Crop, Detections Classes Replacement, Object Detection Model, Google Gemini, Email Notification, OpenAI, Instance Segmentation Model, LMM For Classification, VLM as Detector, Instance Segmentation Model, Roboflow Dataset Upload, LMM
• outputs: Stability AI Inpainting, Line Counter, Florence-2 Model, Model Monitoring Inference Aggregator, Label Visualization, Florence-2 Model, Corner Visualization, Triangle Visualization, Overlap Filter, Background Color Visualization, Model Comparison Visualization, Byte Tracker, Detections Transformation, Circle Visualization, Perspective Correction, Line Counter, Detections Stitch, Trace Visualization, Byte Tracker, Blur Visualization, Dynamic Zone, Time in Zone, Path Deviation, Byte Tracker, Detections Consensus, Velocity, Detection Offset, Detections Stabilizer, Bounding Rectangle, Detections Filter, Size Measurement, Time in Zone, Roboflow Dataset Upload, Segment Anything 2 Model, Detections Merge, Polygon Visualization, Roboflow Custom Metadata, Mask Visualization, Bounding Box Visualization, Path Deviation, Distance Measurement, Ellipse Visualization, Crop Visualization, Color Visualization, Pixelate Visualization, Stitch OCR Detections, PTZ Tracking (ONVIF).md), Dynamic Crop, Detections Classes Replacement, Halo Visualization, Dot Visualization, Roboflow Dataset Upload

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Path Deviation in version v2 has.

Bindings

• input

  • image (image): not available.
  • detections (Union[instance_segmentation_prediction, object_detection_prediction]): Predictions.
  • triggering_anchor (string): Triggering anchor. Allowed values: CENTER, CENTER_LEFT, CENTER_RIGHT, TOP_CENTER, TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_CENTER, BOTTOM_RIGHT, CENTER_OF_MASS.
  • reference_path (list_of_values): Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].

• output

  • path_deviation_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 Path Deviation in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/path_deviation_analytics@v2",
    "image": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "triggering_anchor": "CENTER",
    "reference_path": "$inputs.expected_path"
}

v1

Class: PathDeviationAnalyticsBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.path_deviation.v1.PathDeviationAnalyticsBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

The PathDeviationAnalyticsBlock is an analytics block designed to measure the Frechet distance of tracked objects from a user-defined reference path. The block requires detections to be tracked (i.e. each object must have a unique tracker_id assigned, which persists between frames).

Type identifier

Use the following identifier in step "type" field: roboflow_core/path_deviation_analytics@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
triggering_anchor	str	Point on the detection that will be used to calculate the Frechet distance..	✅
reference_path	List[Any]	Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].	✅

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 Path Deviation in version v1.

• inputs: Keypoint Detection Model, Line Counter, Google Vision OCR, Florence-2 Model, Template Matching, Model Monitoring Inference Aggregator, Florence-2 Model, Overlap Filter, CogVLM, OCR Model, Byte Tracker, Detections Transformation, VLM as Detector, Perspective Correction, Detections Stitch, CSV Formatter, OpenAI, Byte Tracker, Clip Comparison, Dynamic Zone, Multi-Label Classification Model, Object Detection Model, Time in Zone, Path Deviation, Slack Notification, Clip Comparison, Dimension Collapse, Byte Tracker, Detection Offset, Detections Consensus, Detections Stabilizer, Velocity, YOLO-World Model, OpenAI, Llama 3.2 Vision, Bounding Rectangle, Detections Filter, Anthropic Claude, Size Measurement, Time in Zone, Detections Merge, Moondream2, Segment Anything 2 Model, Webhook Sink, Roboflow Dataset Upload, Roboflow Custom Metadata, Single-Label Classification Model, Buffer, Path Deviation, VLM as Classifier, Local File Sink, Twilio SMS Notification, Stitch OCR Detections, PTZ Tracking (ONVIF).md), Dynamic Crop, Detections Classes Replacement, Object Detection Model, Google Gemini, Email Notification, OpenAI, Instance Segmentation Model, LMM For Classification, VLM as Detector, Instance Segmentation Model, Roboflow Dataset Upload, LMM
• outputs: Stability AI Inpainting, Line Counter, Florence-2 Model, Model Monitoring Inference Aggregator, Label Visualization, Florence-2 Model, Corner Visualization, Triangle Visualization, Overlap Filter, Background Color Visualization, Model Comparison Visualization, Byte Tracker, Detections Transformation, Circle Visualization, Perspective Correction, Line Counter, Detections Stitch, Trace Visualization, Byte Tracker, Blur Visualization, Dynamic Zone, Time in Zone, Path Deviation, Byte Tracker, Detections Consensus, Velocity, Detection Offset, Detections Stabilizer, Bounding Rectangle, Detections Filter, Size Measurement, Time in Zone, Roboflow Dataset Upload, Segment Anything 2 Model, Detections Merge, Polygon Visualization, Roboflow Custom Metadata, Mask Visualization, Bounding Box Visualization, Path Deviation, Distance Measurement, Ellipse Visualization, Crop Visualization, Color Visualization, Pixelate Visualization, Stitch OCR Detections, PTZ Tracking (ONVIF).md), Dynamic Crop, Detections Classes Replacement, Halo Visualization, Dot Visualization, Roboflow Dataset Upload

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Path Deviation in version v1 has.

Bindings

• input

  • metadata (video_metadata): not available.
  • detections (Union[instance_segmentation_prediction, object_detection_prediction]): Predictions.
  • triggering_anchor (string): Point on the detection that will be used to calculate the Frechet distance..
  • reference_path (list_of_values): Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].

• output

  • path_deviation_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 Path Deviation in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/path_deviation_analytics@v1",
    "metadata": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "triggering_anchor": "CENTER",
    "reference_path": "$inputs.expected_path"
}