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: LMM, Size Measurement, Dynamic Zone, Detections Transformation, Model Monitoring Inference Aggregator, Detections Classes Replacement, Object Detection Model, Line Counter, Local File Sink, Email Notification, Time in Zone, Anthropic Claude, Google Gemini, Florence-2 Model, Multi-Label Classification Model, Segment Anything 2 Model, OCR Model, Byte Tracker, Time in Zone, Roboflow Custom Metadata, Florence-2 Model, LMM For Classification, Overlap Filter, Detection Offset, Perspective Correction, Google Vision OCR, EasyOCR, Llama 3.2 Vision, Detections Stabilizer, SAM 3, Slack Notification, Velocity, Clip Comparison, Stitch OCR Detections, Byte Tracker, OpenAI, Roboflow Dataset Upload, Path Deviation, Template Matching, CogVLM, Email Notification, VLM as Detector, Instance Segmentation Model, Byte Tracker, OpenAI, Clip Comparison, Detections Stitch, Keypoint Detection Model, Object Detection Model, YOLO-World Model, Detections Merge, Moondream2, Seg Preview, Path Deviation, Roboflow Dataset Upload, Dimension Collapse, Buffer, Instance Segmentation Model, Detections Filter, OpenAI, VLM as Classifier, Bounding Rectangle, CSV Formatter, Time in Zone, Detections Combine, Twilio SMS Notification, VLM as Detector, Webhook Sink, Detections Consensus, Single-Label Classification Model, PTZ Tracking (ONVIF).md), Dynamic Crop
• outputs: Polygon Visualization, Roboflow Dataset Upload, Background Color Visualization, Size Measurement, Dynamic Zone, Path Deviation, Detections Transformation, Corner Visualization, Model Monitoring Inference Aggregator, Detections Classes Replacement, Distance Measurement, Trace Visualization, Mask Visualization, Line Counter, Bounding Box Visualization, Model Comparison Visualization, Byte Tracker, Pixelate Visualization, Time in Zone, Florence-2 Model, Detections Stitch, Ellipse Visualization, Triangle Visualization, Segment Anything 2 Model, Crop Visualization, Detections Merge, Icon Visualization, Byte Tracker, Color Visualization, Roboflow Dataset Upload, Path Deviation, Label Visualization, Time in Zone, Roboflow Custom Metadata, Florence-2 Model, Blur Visualization, Dot Visualization, Overlap Filter, Detections Filter, Circle Visualization, Detection Offset, Bounding Rectangle, Line Counter, Detections Stabilizer, Detections Combine, Time in Zone, Dynamic Crop, Velocity, Detections Consensus, Stitch OCR Detections, Byte Tracker, PTZ Tracking (ONVIF).md), Halo Visualization, Stability AI Inpainting, Perspective Correction

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: LMM, Size Measurement, Dynamic Zone, Detections Transformation, Model Monitoring Inference Aggregator, Detections Classes Replacement, Object Detection Model, Line Counter, Local File Sink, Email Notification, Time in Zone, Anthropic Claude, Google Gemini, Florence-2 Model, Multi-Label Classification Model, Segment Anything 2 Model, OCR Model, Byte Tracker, Time in Zone, Roboflow Custom Metadata, Florence-2 Model, LMM For Classification, Overlap Filter, Detection Offset, Perspective Correction, Google Vision OCR, EasyOCR, Llama 3.2 Vision, Detections Stabilizer, SAM 3, Slack Notification, Velocity, Clip Comparison, Stitch OCR Detections, Byte Tracker, OpenAI, Roboflow Dataset Upload, Path Deviation, Template Matching, CogVLM, Email Notification, VLM as Detector, Instance Segmentation Model, Byte Tracker, OpenAI, Clip Comparison, Detections Stitch, Keypoint Detection Model, Object Detection Model, YOLO-World Model, Detections Merge, Moondream2, Seg Preview, Path Deviation, Roboflow Dataset Upload, Dimension Collapse, Buffer, Instance Segmentation Model, Detections Filter, OpenAI, VLM as Classifier, Bounding Rectangle, CSV Formatter, Time in Zone, Detections Combine, Twilio SMS Notification, VLM as Detector, Webhook Sink, Detections Consensus, Single-Label Classification Model, PTZ Tracking (ONVIF).md), Dynamic Crop
• outputs: Polygon Visualization, Roboflow Dataset Upload, Background Color Visualization, Size Measurement, Dynamic Zone, Path Deviation, Detections Transformation, Corner Visualization, Model Monitoring Inference Aggregator, Detections Classes Replacement, Distance Measurement, Trace Visualization, Mask Visualization, Line Counter, Bounding Box Visualization, Model Comparison Visualization, Byte Tracker, Pixelate Visualization, Time in Zone, Florence-2 Model, Detections Stitch, Ellipse Visualization, Triangle Visualization, Segment Anything 2 Model, Crop Visualization, Detections Merge, Icon Visualization, Byte Tracker, Color Visualization, Roboflow Dataset Upload, Path Deviation, Label Visualization, Time in Zone, Roboflow Custom Metadata, Florence-2 Model, Blur Visualization, Dot Visualization, Overlap Filter, Detections Filter, Circle Visualization, Detection Offset, Bounding Rectangle, Line Counter, Detections Stabilizer, Detections Combine, Time in Zone, Dynamic Crop, Velocity, Detections Consensus, Stitch OCR Detections, Byte Tracker, PTZ Tracking (ONVIF).md), Halo Visualization, Stability AI Inpainting, Perspective Correction

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"
}