Detecting objects and concepts - Amazon Rekognition

Detecting objects and concepts

This section provides information for detecting labels in images and videos with Amazon Rekognition Image and Amazon Rekognition Video.

A label or a tag is an object or concept (including scenes and actions) found in an image or video based on its contents. For example, an image of people on a tropical beach may contain labels such as Palm Tree (object), Beach (scene), Running (action), and Outdoors (concept).

Labels supported by Rekognition label detection operations

  • To download the latest list of labels and object bounding boxes supported by Amazon Rekognition, click here.

  • To download the previous list of labels and object bounding boxes, click here.


Amazon Rekognition makes gender binary (man, woman, girl, etc.) predictions based on the physical appearance of a person in a particular image. This kind of prediction is not designed to categorize a person’s gender identity, and you shouldn't use Amazon Rekognition to make such a determination. For example, a male actor wearing a long-haired wig and earrings for a role might be predicted as female.

Using Amazon Rekognition to make gender binary predictions is best suited for use cases where aggregate gender distribution statistics need to be analyzed without identifying specific users. For example, the percentage of users who are women compared to men on a social media platform.

We don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services.

Amazon Rekognition returns labels in English. You can use Amazon Translate to translate English labels into other languages.

The following diagram shows shows the order for calling operations, depending on your goals for using the Amazon Rekognition Image or Amazon Rekognition Video operations:

Diagram showing image and video analysis workflows with stored and streaming video processing.

Label Response Objects

Bounding Boxes

Amazon Rekognition Image and Amazon Rekognition Video can return the bounding box for common object labels such as cars, furniture, apparel or pets. Bounding box information isn't returned for less common object labels. You can use bounding boxes to find the exact locations of objects in an image, count instances of detected objects, or to measure an object's size using bounding box dimensions.

For example, in the following image, Amazon Rekognition Image is able to detect the presence of a person, a skateboard, parked cars and other information. Amazon Rekognition Image also returns the bounding box for a detected person, and other detected objects such as cars and wheels.

Person doing a skateboard stunt between parked cars on a city street.

Confidence Score

Amazon Rekognition Video and Amazon Rekognition Image provide a percentage score for how much confidence Amazon Rekognition has in the accuracy of each detected label.


Amazon Rekognition Image and Amazon Rekognition Video use a hierarchical taxonomy of ancestor labels to categorize labels. For example, a person walking across a road might be detected as a Pedestrian. The parent label for Pedestrian is Person. Both of these labels are returned in the response. All ancestor labels are returned and a given label contains a list of its parent and other ancestor labels. For example, grandparent and great grandparent labels, if they exist. You can use parent labels to build groups of related labels and to allow querying of similar labels in one or more images. For example, a query for all Vehicles might return a car from one image and a motor bike from another.


Amazon Rekognition Image and Amazon Rekognition Video return information on label categories. Labels are part of categories that group individual labels together based on common functions and contexts, such as ‘Vehicles and Automotive’ and ‘Food and Beverage’. A label category can be a subcategory of a parent category.


In addition to returning labels, Amazon Rekognition Image and Amazon Rekognition Video returns any aliases associated with the label. Aliases are labels with the same meaning or labels that are visually interchangeable with the primary label returned. For example, ‘Cell Phone’ is an alias of ‘Mobile Phone’.

In previous versions, Amazon Rekognition Image returned aliases like 'Cell Phone' in the same list of primary label names that contained 'Mobile Phone'. Amazon Rekognition Image now returns 'Cell Phone' in a field called "aliases" and 'Mobile Phone' in the list of primary label names. If your appliction relies on the structures returned by a previous version of Rekognition, you may need to transform the current response returned by the image or video label detection operations into the previous response structure, where all labels and aliases are returned as primary labels.

If you need to transform the current response from the DetectLabels API (for label detection in images) into the previous response structure, see the code example in Transforming the DetectLabels response.

If you need to transform the current response from the GetLabelDetection API (for label detection in stored videos) into the previous response structure, see the code example in Transforming the GetLabelDetection Response.

Image Properties

Amazon Rekognition Image returns information about image quality (sharpness, brightness, and contrast) for the entire image. Sharpness and brightness are also returned for the foreground and background of the image. Image Properties can also be used to detect dominant colors of the entire image, foreground, background, and objects with bounding boxes.

An image of a green car on a city street, with the car surrounded by a bounding box.

The following is an example of the ImageProperties data contained in the response of a DetectLabels operation for the proceeding image:

Table showing dominant colors and image quality metrics for an entire image, foreground, background, and an example object with a bounding box.

Image Properties isn't available for Amazon Rekognition Video.

Model Version

Amazon Rekognition Image and Amazon Rekognition Video both return the version of the label detection model used to detect labels in an image or stored video.

Inclusion and Exclusion Filters

You can filter the results returned by Amazon Rekognition Image and Amazon Rekognition Video label detection operations. Filter results by providing filtration criteria for labels and categories. Label filters can be inclusive or exclusive.

See Detecting labels in an image for more information regarding filtration of results obtained with DetectLabels.

See Detecting labels in a video for more information regarding filtration of results obtained by GetLabelDetection.

Sorting and Aggregating Results

Results obtained from certain Amazon Rekognition Video operations can be sorted and aggregated according to timestamps and video segments. When retrieving the results of a Label Detection or Content Moderation job, with GetLabelDetection or GetContentModeration respectively, you can use the SortBy and AggregateBy arguments to specify how you want your results returned. You can use SortBy with TIMESTAMP or NAME (Label names), and use TIMESTAMPS or SEGMENTS with the AggregateBy argument.