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

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

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[object_detection_prediction, instance_segmentation_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"
}