Detection Event Log

Class: DetectionEventLogBlockV1

Source: inference.core.workflows.core_steps.analytics.detection_event_log.v1.DetectionEventLogBlockV1

This block maintains a log of detection events from tracked objects. It records when each object was first seen, its class, and the last time it was seen.Objects must be seen for a minimum number of frames (frame_threshold) before being logged. Stale events (not seen for stale_frames) are removed during periodic cleanup (every flush_interval frames).

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
frame_threshold	int	Number of frames an object must be seen before being logged..	✅
flush_interval	int	How often (in frames) to run the cleanup operation for stale events..	✅
stale_frames	int	Remove events that haven't been seen for this many frames..	✅
reference_timestamp	float	Unix timestamp when the video started. When provided, absolute timestamps (first_seen_timestamp, last_seen_timestamp) are included in output, calculated as relative time + reference_timestamp..	✅
fallback_fps	float	Fallback FPS to use when video metadata does not provide FPS information. Used to calculate relative timestamps..	✅

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 Detection Event Log in version v1.

• inputs: Instance Segmentation Model, Detections Consensus, Instance Segmentation Model, Motion Detection, Object Detection Model, Detections Stitch, VLM as Detector, Detections Merge, Detection Offset, Byte Tracker, Byte Tracker, Dynamic Zone, Overlap Filter, Google Vision OCR, Time in Zone, Path Deviation, Bounding Rectangle, Detection Event Log, Detections Transformation, YOLO-World Model, Detections Combine, Segment Anything 2 Model, Detections Classes Replacement, Template Matching, VLM as Detector, SAM 3, Time in Zone, Byte Tracker, Dynamic Crop, Velocity, Line Counter, Time in Zone, Detections Stabilizer, Moondream2, OCR Model, Seg Preview, SAM 3, PTZ Tracking (ONVIF).md), Path Deviation, Perspective Correction, EasyOCR, SAM 3, Detections List Roll-Up, Detections Filter, Object Detection Model
• outputs: Morphological Transformation, Email Notification, Motion Detection, Detections Stitch, Anthropic Claude, Detections Merge, Pixel Color Count, Keypoint Detection Model, Reference Path Visualization, Stitch OCR Detections, Camera Focus, Stitch Images, Stability AI Outpainting, Time in Zone, Bounding Rectangle, Roboflow Dataset Upload, Detections Transformation, Identify Outliers, Dynamic Crop, Time in Zone, Triangle Visualization, Dot Visualization, Crop Visualization, PTZ Tracking (ONVIF).md), Twilio SMS Notification, Perspective Correction, Twilio SMS/MMS Notification, Pixelate Visualization, Detections Consensus, Roboflow Dataset Upload, Object Detection Model, SIFT Comparison, Byte Tracker, Model Comparison Visualization, Halo Visualization, Byte Tracker, Slack Notification, Dynamic Zone, Image Contours, Background Color Visualization, Image Blur, Mask Visualization, Path Deviation, Corner Visualization, Color Visualization, Line Counter Visualization, Ellipse Visualization, Icon Visualization, Velocity, Image Slicer, Detections Stabilizer, Absolute Static Crop, Stability AI Inpainting, Distance Measurement, Detections Filter, Blur Visualization, Instance Segmentation Model, Florence-2 Model, Instance Segmentation Model, Keypoint Visualization, Roboflow Custom Metadata, Detection Offset, Image Threshold, Anthropic Claude, Email Notification, Overlap Filter, Image Slicer, Detection Event Log, Image Preprocessing, Florence-2 Model, Time in Zone, Byte Tracker, Path Deviation, Detections List Roll-Up, Grid Visualization, Line Counter, Object Detection Model, Trace Visualization, QR Code Generator, Webhook Sink, Background Subtraction, Bounding Box Visualization, Label Visualization, Circle Visualization, Dominant Color, Size Measurement, Classification Label Visualization, Detections Combine, Segment Anything 2 Model, Detections Classes Replacement, Model Monitoring Inference Aggregator, Line Counter, Polygon Visualization, SIFT Comparison, Keypoint Detection Model, Identify Changes, Text Display

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Detection Event Log in version v1 has.

Bindings

• input

  • image (image): Reference to the image for video metadata (frame number, timestamp)..
  • detections (Union[object_detection_prediction, instance_segmentation_prediction]): Tracked detections from byte tracker (must have tracker_id)..
  • frame_threshold (integer): Number of frames an object must be seen before being logged..
  • flush_interval (integer): How often (in frames) to run the cleanup operation for stale events..
  • stale_frames (integer): Remove events that haven't been seen for this many frames..
  • reference_timestamp (float): Unix timestamp when the video started. When provided, absolute timestamps (first_seen_timestamp, last_seen_timestamp) are included in output, calculated as relative time + reference_timestamp..
  • fallback_fps (float): Fallback FPS to use when video metadata does not provide FPS information. Used to calculate relative timestamps..

• output

  • event_log (dictionary): Dictionary.
  • 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.
  • total_logged (integer): Integer value.
  • total_pending (integer): Integer value.

Example JSON definition of step Detection Event Log in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/detection_event_log@v1",
    "image": "$inputs.image",
    "detections": "$steps.byte_tracker.tracked_detections",
    "frame_threshold": 5,
    "flush_interval": 30,
    "stale_frames": 150,
    "reference_timestamp": 1726570875.0,
    "fallback_fps": 1.0
}