Get object detection insights

Article
10/09/2024

Object detection

Azure AI Video Indexer detects objects in videos such as cars, handbags and backpacks, and laptops.

Supported objects

airplane
apple
backpack
banana
baseball glove
bed
bench
bicycle
boat
book
bottle
bowl
broccoli
bus
cake

car
carrot
cell phone
chair
clock
computer mouse
couch
cup
dining table
donut
fire hydrant
fork
frisbee

hair dryer
handbag
hot dog
keyboard
kite
knife
laptop
microwave
motorcycle
computer mouse
necktie
orange
oven
parking meter
pizza
potted plant

sandwich
scissors
sink
skateboard
skis
snowboard
spoon
sports ball
stop sign
suitcase
surfboard
teddy bear

tennis racket
toaster
toilet
toothbrush
traffic light
train
umbrella
vase
wine glass

View the insight JSON with the web portal

Once you have uploaded and indexed a video, insights are available in JSON format for download using the web portal.

Select the Library tab.
Select media you want to work with.
Select Download and the Insights (JSON). The JSON file opens in a new browser tab.
Look for the key pair described in the example response.

Use the API

Use the Get Video Index request. We recommend passing &includeSummarizedInsights=false.
Look for the key pairs described in the example response.

Example response

Detected and tracked objects appear under "detected Objects" in the downloaded insights.json file. Every time a unique object is detected, it's given an ID. That object is also tracked, meaning that the model watches for the detected object to return to the frame. If it does, another instance is added to the instances for the object with different start and end times.

In this example, the first car was detected and given an ID of 1 since it was also the first object detected. Then, a different car was detected and that car was given the ID of 23 since it was the 23rd object detected. Later, the first car appeared again and another instance was added to the JSON. Here's the resulting JSON:

detectedObjects: [
    {
    id: 1,
    type: "Car",
    thumbnailId: "1c0b9fbb-6e05-42e3-96c1-abe2cd48t33",
    displayName: "car",
    wikiDataId: "Q1420",
    instances: [
        {
        confidence: 0.468,
        adjustedStart: "0:00:00",
        adjustedEnd: "0:00:02.44",
        start: "0:00:00",
        end: "0:00:02.44"
        },
        {
        confidence: 0.53,
        adjustedStart: "0:03:00",
        adjustedEnd: "0:00:03.55",
        start: "0:03:00",
        end: "0:00:03.55"
        }    
    ]
    },
    {
    id: 23,
    type: "Car",
    thumbnailId: "1c0b9fbb-6e05-42e3-96c1-abe2cd48t34",
    displayName: "car",
    wikiDataId: "Q1420",
    instances: [
        {
        confidence: 0.427,
        adjustedStart: "0:00:00",
        adjustedEnd: "0:00:14.24",
        start: "0:00:00",
        end: "0:00:14.24"
        }    
    ]
    }
]

Key	Definition
ID	Incremental number of IDs of the detected objects in the media file
Type	Type of objects, for example, Car
ThumbnailID	GUID representing a single detection of the object
displayName	Name to be displayed in the VI portal experience
WikiDataID	A unique identifier in the WikiData structure
Instances	List of all instances that were tracked
Confidence	A score between 0-1 indicating the object detection confidence
adjustedStart	adjusted start time of the video when using the editor
adjustedEnd	adjusted end time of the video when using the editor
start	the time that the object appears in the frame
end	the time that the object no longer appears in the frame

Components

No components are defined for object detection.

Transparency notes

Important

It is important to read the transparency note overview for all VI features. Each insight also has transparency notes of its own:

There are up to 20 detections per frame for standard and advanced processing and 35 tracks per class.
Object size shouldn't be greater than 90 percent of the frame. Very large objects that consistently span over a large portion of the frame might not be recognized.
Small or blurry objects can be hard to detect. They can either be missed or misclassified (wine glass, cup).
Objects that are transient and appear in very few frames might not be recognized.
Other factors that might affect the accuracy of the object detection include low light conditions, camera motion, and occlusions.
Azure AI Video Indexer supports only real world objects. There's no support for animation or CGI. Computer generated graphics (such as news-stickers) might produce strange results.
Binders, brochures, and other written materials tend to be detected as "book."

Sample code

See all samples for VI

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