Time in Zone

v2

Class: TimeInZoneBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.time_in_zone.v2.TimeInZoneBlockV2

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 TimeInZoneBlock is an analytics block designed to measure time spent by objects in a zone. The block requires detections to be tracked (i.e. each object must have unique tracker_id assigned, which persists between frames)

Type identifier

Use the following identifier in step "type" field: roboflow_core/time_in_zone@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
zone	List[Any]	Coordinates of the target zone..	✅
triggering_anchor	str	The point on the detection that must be inside the zone..	✅
remove_out_of_zone_detections	bool	If true, detections found outside of zone will be filtered out..	✅
reset_out_of_zone_detections	bool	If true, detections found outside of zone will have time reset..	✅

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 Time in Zone in version v2.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Time in Zone in version v2 has.

Bindings

• input

  • image (image): The input image for this step..
  • detections (Union[instance_segmentation_prediction, object_detection_prediction]): Model predictions to calculate the time spent in zone for..
  • zone (list_of_values): Coordinates of the target zone..
  • triggering_anchor (string): The point on the detection that must be inside the zone..
  • remove_out_of_zone_detections (boolean): If true, detections found outside of zone will be filtered out..
  • reset_out_of_zone_detections (boolean): If true, detections found outside of zone will have time reset..

• output

  • timed_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 Time in Zone in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/time_in_zone@v2",
    "image": "$inputs.image",
    "detections": "$steps.object_detection_model.predictions",
    "zone": [
        [
            100,
            100
        ],
        [
            100,
            200
        ],
        [
            300,
            200
        ],
        [
            300,
            100
        ]
    ],
    "triggering_anchor": "CENTER",
    "remove_out_of_zone_detections": true,
    "reset_out_of_zone_detections": true
}

v1

Class: TimeInZoneBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.time_in_zone.v1.TimeInZoneBlockV1

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 TimeInZoneBlock is an analytics block designed to measure time spent by objects in a zone. The block requires detections to be tracked (i.e. each object must have unique tracker_id assigned, which persists between frames)

Type identifier

Use the following identifier in step "type" field: roboflow_core/time_in_zone@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
zone	List[Any]	Coordinates of the target zone..	✅
triggering_anchor	str	The point on the detection that must be inside the zone..	✅
remove_out_of_zone_detections	bool	If true, detections found outside of zone will be filtered out..	✅
reset_out_of_zone_detections	bool	If true, detections found outside of zone will have time reset..	✅

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 Time in Zone in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Time in Zone in version v1 has.

Bindings

• input

  • image (image): The input image for this step..
  • metadata (video_metadata): not available.
  • detections (Union[instance_segmentation_prediction, object_detection_prediction]): Model predictions to calculate the time spent in zone for..
  • zone (list_of_values): Coordinates of the target zone..
  • triggering_anchor (string): The point on the detection that must be inside the zone..
  • remove_out_of_zone_detections (boolean): If true, detections found outside of zone will be filtered out..
  • reset_out_of_zone_detections (boolean): If true, detections found outside of zone will have time reset..

• output

  • timed_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 Time in Zone in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/time_in_zone@v1",
    "image": "$inputs.image",
    "metadata": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "zone": "$inputs.zones",
    "triggering_anchor": "CENTER",
    "remove_out_of_zone_detections": true,
    "reset_out_of_zone_detections": true
}