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

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

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"
}