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"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring," a Presentation from Camio

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For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-maslan

For more information about embedded vision, please visit:
http://www.embedded-vision.com

Carter Maslan, CEO of Camio, presents the "Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring" tutorial at the May 2017 Embedded Vision Summit.

Network cameras and other edge devices are collecting ever-more video – far more than can be economically transported to the cloud. This argues for putting intelligence in edge devices. But the cloud offers unique, valuable capabilities, such as aggregating information from multiple cameras, applying state-of-the-art algorithms, and providing users with access to their data anywhere, any time.

Camio uses a combination of machine learning at the edge (in network cameras and network video recorders) and in the cloud to generate alerts, highlight the most significant events captured by a camera, and to let users search for events of interest. In this talk, Maslan explores the trade-offs between edge and cloud processing for systems that extract meaning from video, and explains how the two approaches can be combined to create big opportunities.

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"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring," a Presentation from Camio

  1. 1. Copyright © 2017 Camio 1 real-time video search Carter Maslan May 2017 Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring
  2. 2. Copyright © 2017 Camio 2
  3. 3. Copyright © 2017 Camio 3 In deluge of video, less is more
  4. 4. Copyright © 2017 Camio 4 sport utility vehicle Camio makes video simple & useful cameras that learn cloud service client firmware works with any camera on any network
  5. 5. Copyright © 2017 Camio 5 • Compute Power deep learning + inexpensive cameras + cloud-connected compute capacity • Social Expectation transparency & accountability + security threats + always-connected mobility Why now? First time feasible & expected
  6. 6. Copyright © 2017 Camio 6 Instant answers, not hours of video
  7. 7. Copyright © 2017 Camio 7 Historical challenges Problem Limited edge compute power Expensive cloud compute Limited Internet bandwidth False positives Diverse workloads Mitigation RTSP streams via Camio Box Real-time event ranking Metadata-first pipeline Adaptive motion filters Hooks for callback extensions
  8. 8. Copyright © 2017 Camio 8 Context is important Precision & recall aren’t just about Edge vs. Cloud
  9. 9. Copyright © 2017 Camio 9 Metadata-first video pipeline
  10. 10. Copyright © 2017 Camio 10 • Uploads metadata [and video] • Modular cloud / edge compatible components • General-purpose task managers • Validated firmware updates • Remote logging Plumbing is important Effort for Edge + Cloud is 90% plumbing
  11. 11. Copyright © 2017 Camio 11 The Camio Box and Cloud Pipelines Removal of spurious motion Motion Detection Events, time compression Box Cloud Indexing, storage, search ML: important for user? ML: scene recognition ML: object recognition Current
  12. 12. Copyright © 2017 Camio 12 The Camio Box and Cloud Pipelines Removal of spurious motion Motion Detection Events, time compression Box Cloud Indexing, storage, search ML: important for user? ML: scene recognition ML: object recognition Next release
  13. 13. Copyright © 2017 Camio 13 The Camio Box and Cloud Pipelines Removal of spurious motion Motion Detection Events, time compression Box Cloud Indexing, storage, search ML: important for user? ML: scene recognition ML: object recognition ~ June
  14. 14. Copyright © 2017 Camio 14 The Camio Box and Cloud Pipelines Removal of spurious motion Motion Detection Events, time compression Box Cloud Indexing, storage, search ML: important for user? ML: scene recognition ML: object recognition ~ Summer
  15. 15. Copyright © 2017 Camio 15 • Edge • Fat content ML • High frame rate ML • Sequential operations • Use-it-or-lose it state • Counters (and math) How to decide what’s Edge vs. Cloud? • Cloud • Parallelizable spikes • Long async operations • Context & feedback loops • Shared ML models • Services & hooks Driven by available hardware, quality, cost and bandwidth
  16. 16. Copyright © 2017 Camio 16 • User-specific query parsers • Parser is augmented by labels observed in user’s own history. • Precedence and boolean logic inferred from known cameras, zones, colors, direction of movement, etc. • Unrecognized query terms still applied as filters (e.g., OCR text). • Natural Language Parser is used for dates, times, relative ranges. • Minimizes need for search operators (only “v:{{view_token}}”). • /api/search • Text-focused search is voice-input friendly. • E.g., https://camio.com/api/search?text=people+8am+to+10am • JSON supports flexible metadata/media responses. How Camio Search Works
  17. 17. Copyright © 2017 Camio 17 Example hardware VM Box ● 3 1080p cams ● ARM A53 ● Quad-core ● 2 GHz ● 2 GB RAM ● Gbit Ethernet Box Pro ● 16 1080p cams ● Intel i5-5200U ● Dual core (8 equiv) ● 2.2 GHz ● 8 GB RAM ● Gbit Ethernet Box Virtual ● Linux VM ● Deploy on local hardware ● Grow with the need ● Download and go!
  18. 18. Copyright © 2017 Camio 18 • https://www.embedded-vision.com/platinum- members/embedded-vision-alliance/embedded-vision- training/videos/pages/feb-2017-member-meeting-camio • https://camio.com/metadata • https://api.camio.com Resources
  19. 19. Copyright © 2017 Camio 19 real-time video search Thank You Making real-time information from real-world events useful and accessible to people, apps, and services via intelligent video monitoring of any video source.

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