Integration¶

IRIS+ Professional supports multiple integration methods to connect with external systems for event notification and data streaming. The main options are:

Webhook: Send detection events to any HTTP endpoint for real-time integration.
Kafka: Stream detection results to downstream consumers using a scalable message bus.
Genetec Security Center: See Genetec Security Center Integration for details on sending events to Genetec SC.
Milestone XProtect: See Milestone XProtect Integration for details on sending analytics events to Milestone XProtect.

Choose the integration method that best fits your workflow and target system.

Webhook¶

Queries can be configured to send detections to a Webhook URL.

Open the query you want to integrate with a Webhook.
Select the Alarm Integration tab in the query editor.
Under Webhook, tick Enable, then fill in the following fields:

URL: The URL of the Webhook.
Custom header key: The key of the custom header.
Custom header value: The value of the custom header.

Kafka¶

Query results are stored in Kafka and can be accessed directly by downstream components. Different query types may generate result sets with varying data schemas. This section documents the most common output data schemas used across query types.

Detection¶

The following query types return Detection records:

Advanced Object Search
Free Text Search
Free Text Search With Threshold
Hazardous Area Detection
Heatmap
Loitering Detection
Object Detection
Object Line Cross Detection
Object Presence Change
Started Object Detection
Stopped Object Detection

This is an example Detection record contained in the kafka record value. There are some other non-documented technical fields.

Detection Record Example

{
   "video_id":"c70bd9e9-4207-4d3e-b38c-386beb562bb3",
   "frame_time":1747811848918,
   "track_id":600,
   "frame_width":1280,
   "frame_height":960,
   "obj_type":"person",
   "box":{
      "x":418,
      "y":145,
      "width":49,
      "height":239
   },
   "confidence":0.5888672,
   "person_attributes":{
      "face_attributes":{
         "key_points":{
            "right_eye":{
               "x":0,
               "y":0
            },
            "left_eye":{
               "x":0,
               "y":0
            },
            "nose":{
               "x":0,
               "y":0
            },
            "mouth_right":{
               "x":0,
               "y":0
            },
            "mouth_left":{
               "x":0,
               "y":0
            }
         },
         "confidence":0.0,
         "box":{
            "x":0,
            "y":0,
            "width":0,
            "height":0
         }
      },
   },
   "vehicle_attributes":{
      "license_plate":{
         "confidence":0.0,
         "licence_plate":""
      }
   },
}

Field Descriptions¶

Some of the fields are technical, we only document the main fields:

video_id: string
A unique identifier for the video from which the frame was extracted. Useful for correlating detections with specific video streams.
frame_time: integer (timestamp in milliseconds)
Epoch timestamp indicating when the frame was captured, in milliseconds since Unix epoch. This allows alignment with other time-based data streams.
track_id: integer
An identifier used to associate detections across frames, i.e., to track the same object over time. Unique per video per object instance.
frame_width: integer
Width of the video frame in pixels. Useful for normalizing coordinates or calculating relative positions.
frame_height: integer
Height of the video frame in pixels. Like frame_width, this helps interpret bounding box dimensions in context.
obj_type: string
The semantic class of the detected object, e.g., "person", "car", etc. Derived from the object detection model.
box: object
Describes the bounding box around the detected object within the frame:
- x: integer — X-coordinate of the top-left corner of the box.
- y: integer — Y-coordinate of the top-left corner of the box.
- width: integer — Width of the bounding box in pixels.
- height: integer — Height of the bounding box in pixels.
confidence: float
A score between 0 and 1 indicating the model’s confidence in the detection. Higher values imply greater certainty.
person_attributes: object
Further details if the object type is person:
- face_attributes: object - filled if the face of the person has been detected:
- key_points: Face key points
- confidence: Confidence of face detection between 0-1
- box: Face bounding box
vehicle_attributes: object
Further details if the object type a vehicle class.

DetectionPair¶

The following query types return DetectionPair records:

Measure Vehicle Speed
Near Miss Detection
Tailgating Detection
Red Light Running

This is an example DetectionPair record contained in the kafka record value.

DetectionPair Record Example

{
  "left": { /* Detection record */ },
  "right": { /* Detection record */ },
  "speed_in_meter_per_sec": 5.2
}

Field Descriptions¶

left: object
A Detection record representing the first element of the pair.
right: object
A Detection record representing the second element of the pair.
speed_in_meter_per_sec: float (optional)
The speed of the object in meters per second, if available.

DetectionSet¶

Currently not used by any query types. It is defined to support some of the future query types.

This is an example DetectionSet record contained in the kafka record value.

DetectionSet Record Example

{
  "detections": [ /* Array of Detection records */ ]
}

Field Descriptions¶

detections: array of object
A list of Detection records forming the set.