2018/4/18 に開催された，AWS Machine Learning Tech Night - April 2018 - の Ranju Das による Amazon Rekognition についての発表資料です． https://awsj-ml.connpass.com/event/83623/

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Ranju Das, Director – Amazon Rekognition
Amazon Rekognition
De e p l e a rn in g -b ase d imag e and vide o analysis

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Put machine learning
in the hands of every
developer and data
scientist
ML @ AWS:
OUR MISSION

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Frameworks &
Infrastructure
GPU (P3 Instances) MobileCPU IoT (Greengrass)
Platform
Services
Application
Services
AWS Deep Learning AMI
AWS ML Stack
Amazon
SageMaker
AWS
DeepLens
TensorFlow GluonApache MXNet Cognitive Toolkit Caffe2 & Caffe PyTorch KerasChainer
Vision
Rekognition
Image
Rekognition
Video
Speech
Polly Transcribe
Language
Translate ComprehendLex

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Amazon Rekognition Image and Video

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Object and scene
Detection
Facial
analysis
Face
comparison
Facial
recognition
Celebrity
recognition
Image
moderation
Amazon Rekognition Image
Deep learning-based image analysis service

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Results:
| IT’S - 97% |
| MONDAY – 99% |
|but – 97% |keep – 96% |
| Smiling – 99% |
DetectText
Amazon Rekognition Image
N O V E M B E R 2 0 1 7
Text in image

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<0.5 second response time
Up to 10M faces
Enable Immediate response
Amazon Rekognition Image
N O V E M B E R 2 0 1 7
Real-time face recognition against tens of millions of faces

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Amazon Rekognition Image
Customers

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How can we apply these
powerful capabilities to video?

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Traditional solutions and limitations
Temporal
information lost
Motion
context lost

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Amazon Rekognition Video
Video analysis

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Amazon Rekognition Video
Stored Video
Amazon S3
Video Live Stream
Amazon Kinesis Video Stream
Media search index
Unsafe video detection
Investigative analysis
Public safety immediate response
Home monitoring
One solution for all

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Media & Entertainment Public Safety Smart Home
Search & Filter Immediate Response
Investigative Analysis]
Monitoring
Amazon Rekognition Video
Primary use cases

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Amazon Rekognition Video
Demo
Traffic Cam - Video Analysis

15. Rekognition Video: Demo

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"Persons": [
{
"Timestamp": number,
"Person":
{
"Index": number,
"BoundingBox":
{
"Width": number,
"Top": number,
"Height": number,
"Left": number
},
"Face":
{
"BoundingBox": { ... },
"Landmarks": { ... },
"Pose": { ... },
"Quality": { ... },
"Confidence": number
}
},
...
GetPersonTracking
StartPersonTracking
Amazon Rekognition Video
Person Tracking

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”InputInformation": {
”KinesisVideo": {
"StreamArn": "string"
"FragmentNumber": "string",
"ServerTimestamp": number,
"ProducerTimestamp": number,
"Frameoffser": number
},
"StreamProcessorInformation":{
"Status": "string"
}
"FaceSearchResponse":[
{
"DetectedFace":{},
"MatchedFaces": [
{
"Similarity":float
"FaceId":"string",
"ExternalImageId":"string",
"Confidence":float
}
]
}
]
StartStreamProcessor
CreateStreamProcessor
Amazon Rekognition Video
Stream Processor
StopStreamProcessor

18. Amazon Rekognition Video
Media and entertainment
Create metadata for celebrities, emotions,
key topics in video with time segments for search
Recommendation engines, ad placement
Automatically detect inappropriate
content, based on market requirements
Extract data in streaming mode to enhance
user engagement

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Amazon Rekognition Video
Use case : Video Search Index
Video Amazon S3 AWS Lambda Amazon Rekognition Video
Amazon Elasticsearch Amazon DynamoDB
1. Video is uploaded
and stored to S3
2. Rekognition Video creates
metadata for celebrities, emotions,
key topics in video with time
segments for search
4. Lambda also pushes
the metadata and confidence
scores into Elasticsearch
3. The output is persisted as
metadata into DynamoDB
to ensure durability
D y n a m i c s e a r c h i n d e x i n g
DAM / MAM

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Amazon Rekognition Video
Launch customers

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Getting started:
https://aws.amazon.com/rekognition/getting-started/
Developer Resources:
https://aws.amazon.com/rekognition/developers/
AWS AI Blog—Amazon Rekognition:
https://aws.amazon.com/blogs/ai/tag/amazon-rekognition/
Ge tting starte d with Amazon R e kog nition

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Thank you.