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Video Ecosystem and some ideas about video big data


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Introduction to Video Ecosystem Mind Map
Video Streaming Platform
Video Ad Tech Platform
Video Player Platform
Video Content Distribution Platform
Video Analytics Platform
Summary of key ideas
Q & A

Published in: Data & Analytics
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Video Ecosystem and some ideas about video big data

  1. 1. Video Ecosystem and some ideas about video big data Trieu Nguyen - Head of Platform at Blueseed Digital My personal email:
  2. 2. Agenda 1. Introduction to Video Ecosystem Mind Map a. Video Streaming Platform b. Video Ad Tech Platform c. Video Player Platform d. Video Content Distribution Platform e. Video Analytics Platform 2. Summary of key ideas 3. Q & A
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  4. 4. The value of video content 1. Retain user loyalty with multimedia content 2. Builds the volume of user traffic very fast 3. Delivers best content UX to your user 4. Give your business a wider marketplace
  5. 5. (1) Video Streaming Platform
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  7. 7. Video Streaming Market Research Report- Global Forecast 2023
  8. 8. (2) Video Advertising Technology Platform
  9. 9. How Video is used for Digital Market → Aha: Viral Clip
  10. 10. Omni-channel Marketing
  11. 11. Is YouTube a new TV, and compete directly with traditional TV ?
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  13. 13. (3) Video Player Platform
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  15. 15. (4) Video Content Distribution Platform
  16. 16. In Video Content Platform, the Recommendation Engine is key feature
  17. 17. (5) Video Big Data Platform
  18. 18. Video Analytics Platform i. What is Video Big Data ? ii. What is Visual Information Analytics and why ? iii. How can we extract value from video ? iv. How we design Video Big Data System ?
  19. 19. Video Big Data — Examples ● Netflix — “Other Movies You May Enjoy” ● YouTube — “Recommended Videos” ● Aventura security  — “Cerebrus Intelligent Video Analytics”
  20. 20. What is Video Big Data ?
  21. 21. Visipedia, short for “Visual Encyclopedia,” Visipedia, is a network of people and machines that is designed to harvest and organize visual information and make it accessible to anyone anywhere ( )
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  23. 23. Pre-trained models + + Metadata User Interface Distributed computing resources. E.g. Servers with/without GPUs. Developers / Scientists Users / Annotators / Analysts Deep Learning libraries ? How do we structure Visual Data processing? ? ? Web, Streaming cameras and other external sources
  24. 24. Set of images Video / Dataset Frame Perform_dataset_extraction perform_detection TF object detection API, CTPN, MTCNN, etc. IndexEntries Region Sun Yosemite perform_indexing Inception, vgg, facenet etc. Regions are 2D bounding boxes on a frame and can be generated via detectors / annotators or provided via UI, REST API or pre existing metadata. Regions also JSON and text metadata. And can be “Materialized” as a separate image. peform_analysis Open Images tags, CRNN text recognition, etc. Async tasks are underlined Each box is a data model Segment Each segment begins at an I-type Keyframe This enables parallel decode/processing of video across multiple machine in chunks. perform_video_segmentation perform_indexing Inception, vgg, facenet etc. IndexEntries stores filenames of numpy arrays containing features and corresponding JSON files. Tubes Sun Yosemite Tubes are sequences of Regions. Tubes can be used to represent set of regions or frames or segment for storing metadata about “tracks”, “clips” etc. perform_segment_decode detect_scenes
  25. 25. REST API + DVAPQL Video, image, frame, region, etc. metadata stored in Relational database Filesystem with images, videos, feature vectors & models. Task queues Segmentation workers Indexing workers Nearest Neighbors workers Video segmenter / decoder / encoder Detection workers Analysis / Annotation workers Image processing workers Export Import / Ingest Scheduler for periodic tasks Data-centric Architecture for Video Big Data Source:
  26. 26. Key takeaways 1. Video Streaming Market is really hot 2. Digital Video Platform is the key to success 3. Big Data is “must-have” system for “Video-First Business” → Video Big Data