Image Recognition Integration Server

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Presentation transcript:

Image Recognition Integration Server IRIS Image Recognition Integration Server

Background

Video Analysis technology

Recent capabilities using deep learning

Speed : up to ~70 frames per second (YOLO-V2 2017) Microsoft ResNet won ILSVRC 2015 with an incredible error rate of 3.6% (Depending on their skill and expertise, humans generally hover around a 5-10% error rate) Speed : up to ~70 frames per second (YOLO-V2 2017) of i From YOLO9000:

Convolution Nets

technologies

Face Detection

Image Captioning

Object Detection

Media Asset Management (MAM)

What is MAM? Software family that: serves as repository of Media Objects - Media files (video, audio, images, captions) - Metadata supports workflow around Media Objects Ingest, Catalog, Transcode, Edit, Distribute, Archive... Large scale (100,000s of large video objects in repository)

Combining MAM and Video Analysis

Problems Video Analysis technology is CPU/RAM/GPU intensive. There are many different models we want to use / rapid evolution.

Need a solution to combine Media Analysis tools with MAM: Scale many parallel jobs. On flexible compute infrastructure (add machines on demand on the cloud). Using many different analysis models and tools (Torch, Tensorflow, Caffe).

Image Recognition Integration Server IRIS Image Recognition Integration Server

Software architecture allowing consumption of: Elastic media analysis engines. Easily extensible in isolated containers for each media analysis engine. Low coupling between MAM and Media Analysis Engines.

IRIS Detection Server Client Dalet API Task Manger Worker Queues REST request API call Task Manger Queues Worker REST requests Celery TASK Celery TASK API call

Support Various detection technologies

IRIS Detection Server Task Manger Worker Queues REST requests Celery TASK Celery TASK API call

IRIS Detection Server Task Manger Worker Worker Queues REST requests Celery TASK Celery TASK API call

Scalability and Stability

IRIS Detection Server Queues Task Manger Worker Worker Worker Worker REST requests Celery TASK Celery TASK

IRIS Detection Server Task Manger Task Manger Task Manger Queues Worker Worker Worker Worker Celery TASK Celery TASK REST requests

Friendly Interface

Technology Used

Result

Detection server Manger- RESTful API Yolo Worker Manger- celery

Video

Manger got the job and sended task to queue Job received from client 2 Manger got the job and sended task to queue 1 Job received from client 3 Worker start task

6 5 4 Job received the results Manger handle the results and pass to the Detection server 4 Task finished