Reference

Analysis

IRIS+ Professional provides a wide range of analysis capabilities, including video indexing, object detection, and classification. The analysis is performed on the video stream from cameras and videos added to the system.

Analysis Parameters

The analysis parameters can be configured for each camera or video added to the system. The parameters are divided into several sections, each with its own set of options.

Note

Note that currently, it is not possible to edit indexing parameters after the camera has been added. If you need to change them, you will have to delete the camera and add it again with the new parameters.

IndexingGeneric attributeFace recognitionLicence plateBackground

Here you can set the parameters for indexing the video.

• Detector FPS (4 by default): The number of frames the detector will analyse per second.

Warning

The higher the FPS, the more frames the detector is able to analyse per second. Note, however, that this will also increase GPU usage. The default value of 4 FPS is a good compromise between accuracy and processing needs. Consult the hardware requirements for more information.

Enables the extraction of attributes from the video stream, for all object types (on by default).

• Number of feature vectors (2 by default): The number of feature vectors depends on the number of objects in the video. In case of a scene with low / rare activity, leave it as default. As the number of objects increases, you may consider increasing the number of feature vectors so that no objects are missed.

Enables the extraction of face attributes from the video stream (off by default).

• Number of feature vectors (2 by default): The desired number of feature vectors depends on the number of faces in the video: In case of a scene with low activity, leave it as default. As the number of faces increases, you may consider increasing the number of feature vectors so that no faces are missed.

This feature is currently unavailable for editing. It will be supported in a future release.

Enables the extraction of attributes from the the environment around classifiable objects (on by default). It is used for detecting changes in the background, such as movement or changes in lighting or environmental conditions.

• Max background vector calculations per frame (1 by default): The optimal number of analysed vectors depends on how likely it is for the background to change; If the background is expected to remain mostly static, leave it as default. If the background changes frequently or drastically (Such as in case of a drone footage, or PTZ camera, where the enviroment continously changes due to camera movement), set it to 2 or more, as more frequent calculations will be necessary.

Feature vectors

Feature vectors are quantifiable attributes extracted from video streams. They are used to identify objects in the video and can be used for various purposes, such as object tracking, classification, and recognition.

List of Object Types

IRIS+ Professional supports a wide range of object types that can be detected and classified in video streams. These object types can be used in queries to filter detections and to create custom use cases.

• airplane
• apple
• backpack
• banana
• baseball bat
• baseball glove
• bear
• bed
• bench
• bg
• bicycle
• bird
• boat
• book
• bottle
• bowl
• broccoli
• bus
• cake
• car
• carrot
• cat
• cell phone
• chair
• clock
• couch
• cow

• cup
• dining table
• dog
• donut
• elephant
• fire hydrant
• fork
• frisbee
• giraffe
• hair drier
• handbag
• horse
• hot dog
• keyboard
• kite
• knife
• laptop
• microwave
• motorcycle
• mouse
• orange
• oven
• parking meter
• person
• pizza
• potted plant
• refrigerator

• remote
• sandwich
• scissors
• sheep
• sink
• skateboard
• skis
• snowboard
• spoon
• sports ball
• stop sign
• suitcase
• surfboard
• teddy bear
• tennis racket
• tie
• toaster
• toilet
• toothbrush
• traffic light
• train
• truck
• tv
• umbrella
• vase
• wine glass
• zebra

List of Classifiers

Classifiers, short for Few-Shot Learning (FSL) classifiers, can be utilized in queries to filter detections. They are lightweight, requiring only a few dozen examples for training.

Custom Classifiers

If your needs aren't met by the existing classifiers, feel free to contact us by opening a ticket. Custom classifiers can be developed, in binary format, that can integrate seamlessly with other systems, eliminating the need for a new software release.

Classifier	Applicable Object Type	Classes
Gender	Person	Male Female
Age*	Person	10-year age bands (0-80)
Person on the phone	Person	Talking on the phone Looking at phone None of the above
Hands up	Person	A person with raised hands. A person with non-raised hands.
Emergency vehicle	Car, Truck, Bus	Emergency vehicle Non-emergency vehicle
PPE helmet	Person	Wearing Not wearing
PPE vest	Person	Wearing Not wearing
Person wearing a face mask	Person	Wearing Unsure if wears or not.
Person with a gun*	Person	With a gun Without a gun
Person is smoking*	Person	Smoking Not smoking
Face of person is hidden*	Person	Visible face Hidden face
Gate/door open/closed*	Background	Open Closed
Simple pose*	Person	Standing Sitting Lying down
Fire and smoke	Background	Fire/smoke visible Normal
Construction vehicles	Car, Truck	12 construction vehicle classes*
Fallen person	Person	Fallen person

* Part of an upcoming release.