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