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Similar to "Traffic Speed Control System in the Cloud using Machine Learning" by Albert Bušinskis from Squalio at Auto focused 47th DevClub.lv
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"Traffic Speed Control System in the Cloud using Machine Learning" by Albert Bušinskis from Squalio at Auto focused 47th DevClub.lv
- 1. Traffic Speed Control System In the Cloud Alberts Bušinskis (Garuda) 22.09.2016 With Machine Learning
- 3. Why?
- 4. People killed in road accidents (eurostat)
- 5. We need it yesterday! Collect Data Recognize Operations Export
- 10. Challenges Communication ALPR Architecture • Get Data • Get Stats • Configure • Security • Find Number • Split Chars • Classify • Detect Country • Make it work • Scalability • Resillience • Security
- 11. ALPR • Picture quality • Speed • Accuracy • Find Number • Split Chars • Classify • Find Country
- 12. ALPR • Picture quality • Speed • Accuracy • Find Number • Split Chars • Classify (MNIST) • Find Country
- 13. CNN (Convolutional Neural Networks)
- 14. Which platform? Caffe TensorFlow CNTK Theano Torch DL4J Accord/Aforge Azure ML Amazon ML Brainstorm Mlpack 2 Marvin Neon …
- 16. Microsoft Computational Network Toolkit Performance Flexibility Extensibility
- 18. Plate Finder
- 19. Plate Finder
- 20. Improvements!
- 21. Country Detection // TODO: Neural Networks Regex did not work
- 23. More difficulties with Recognizer Q letters Different fonts I and plate border
- 24. Cloud Webjobs and Cloud Services has locale! Not all instances are similar – Some of them make CNTK fail to “BBBBBB” Dead letter queues SLA? – Web and Business Tiers • We guarantee at least 99.9% of the time customers will have connectivity between their Web or Business Microsoft Azure SQL Database and our Internet gateway. – Basic, Standard, and Premium Tiers • We guarantee at least 99.99% of the time customers will have connectivity between their Basic, Standard, or Premium Microsoft Azure SQL Database and our Internet gateway.
- 25. Cloud Using 3rd party C++ library can be tricky – Luckily, .NET provides classes for memory allocation, pinning byte arrays, etc (Marshal, GCHandle) – The tricky part, however, is making sure the process can run for weeks without a single memory leak while processing gigabytes of data.
- 26. Networking / VPN
- 27. Devices / Networking Communication with SOAP User has single session Multiple modules Each module uses different user Bugs: What does deleteStatistics() do when it returns success? – Nothing
- 28. Joke UTC (uint) localtime (string) area (string) getSystemTime() returns: setSystemTime() takes: UTC (uint) (ex. 1473690358) localtime (string) (ex. “2016-09-12 17:25:58”) area (string)
- 29. Joke UTC - doesn’t matter localtime - Unix timestamp area - cancel datetime offset getSystemTime() returns: setSystemTime() takes: UTC (uint) localtime (string) area (string)
- 30. SCRUM
- 31. Thank you!
Editor's Notes
- I'm from Squalio CC, we do stuff. I do biometrics. We love .Net, Azure, ML Я не вижу большого смысла, приводить в этой презентации куски кода, поэтому будет много картинок
- When I was riding a motorbike in Switzerland: I was lame, roads were safe
- Culture example: Test mode, 15 offences, video with number plate
- People are dying
- Call from management
- The prototype had very basic functionality and Polish devices (modem + recognizer)
- Hardware was working realy bad - support Devices were expensive Recognition quality was not perfect
- The Path is always exciting, why we love doing what we do
- On photo is more difficult then video. Reading docs Australian example Open ALPR
- We startet with Claissify (MNIST). Found a little bit of theory about ALPR. Mixed National Institute of Standards and Technology database Convolutional Neural Network
- For image recognition and computer vision Нейроны в этой сети разбиты на группы (слои) и применяя эту сеть к данным, общее значение подсчитывается последовательно Активация последнего слоя является выходом нейросети Каждый слой имеет свои параметры, которые определяют как активация следующего слоя зависит от активации предведущего Активации внутри одного слоя могут подсчитываться параллельно
- Works inside Azure Webjob
- Multi-machine Multi-GPU Open Source Microsoft uses this for Cognitive Services (Linux)
- Accord.Net/Aforge Got the real data to train NN for Classification
- First version could find a plate Решетки у Ауди (IIIII)
- Motorcycle improvement and cuts Talk a little bit about stats: Classification: 4,21% Segmentation: 23,83% Localisation: 7,84% Overall: 35,88%
- Minuses gets filter out during segmentation
- Story about Q training sets for TQ
- We want to leverage Cloud’s advantages Dynamic / Static VPN (there’s no Static VPN support in Batch processing) Long story with configuration (Cisco support) ** Mobile networking timeouts ** Multiple radars for one
- To avoid conflicts and terminated sessions
- UTC (uint) – UNIX timestamp (ex. 1473690358) localtime (string) – string representation of the local time (ex. “2016-09-12 17:25:58”) area (string) – location (ex. “Latvia”)
- UTC (uint) – doesn’t matter, isn’t used. Just don’t leave blank because it must be a valid uint. localtime (string) – UNIX timestamp of local time as a string. area (string) – if provided, will attempt to cancel the localtime offset, but fails by 1h because it doesn’t account for DST.
- Waterfall to SCRUM was a bit problem (form and no form) Export to Oracle (1 Story Point -> 3 weeks) Requirements and refactoring Dashboard Export: refactoring Pixels cut