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

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

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"
}