1. Image Recognition Integration Server
IRIS
Image Recognition Integration Server

2. Background

3. Video Analysis technology

4. Recent capabilities using deep learning

5. 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:

6. Convolution Nets

13. technologies

14. Face Detection

15. Image Captioning

16. Object Detection

17. Media Asset Management (MAM)

18. 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)

19. Combining MAM and Video Analysis

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

21. 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).

22. Image Recognition Integration Server
IRIS
Image Recognition Integration Server

23. 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.

24. 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

25. Support Various detection technologies

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

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

28. Scalability and Stability

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

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

31. Friendly Interface

33. Technology Used

35. Result

36. Detection server
Manger- RESTful API
Yolo Worker
Manger- celery

37. Video

42. Manger got the job and sended task to queue Job received from client2
Manger got the job and sended task to queue 1
Job received from client 3
Worker start task

43. 6 5 4 Job received the results
Manger handle the results and pass to the Detection server 4
Task finished