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

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

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"
}