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

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

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"
}