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