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

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

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"
}