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Computer Vision
and Deep
Learning with
OpenCV 3 – Part
I
Farshid Pirahansiah
Introduction Video Analysis
 VCA: Video content analysis (Video content analytics) is
the capability of automatically ana...
Introduction Video Analysis II
 Motion Detection
 Video tracking and ego motion estimation
 Based on VCA
 Identificati...
Video Analytic
 Action detection (walking & running)
 Movement detection
 Framework for Behavior Detection based on
eve...
Video Analysis, Tracking
 Motion based multiple object tracking
 Kernel-based tracking
 mean-shift tracking
 Contour t...
Deep Learning – Resources
 OpenCV: Deep Neural Network module
 http://docs.opencv.org/3.1.0/d6/d0f/group__dnn.
html
 ht...
Datasets for Computer vision +
Deep Learning
Google Research: Computer vision + Deep
Learning
1. Open Images Dataset
2. Yo...
Datasets for images
 Deep learning needs large amount of inputs for
training. detecting and classifying objects in static...
Datasets for Video analysis I
1. video is much more time-consuming to annotate
manually than images
 video annotation sys...
Datasets for Video analysis II
 YouTube-8M: A Large and Diverse Labeled
Video Dataset for Video Understanding
Research
 ...
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Computer Vision, Deep Learning, OpenCV

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Published on

Computer Vision, Deep Learning, OpenCV 3, C++, Image Processing, Machine Vision, Artificial Intelligence
Video Understanding, Video Analysis, Video Tracking, Automated Human Behavior Recognition, Event Recognition In Surveillance Video, Video Content Analytics, Time Series Features,
Open Images Dataset, Automatically Caption Images, Inception V3, Convolutional Neural Network (CNN), Caffe, TensorFlow, Theano, Torch
www.pirahansiah.com

Published in: Software

Computer Vision, Deep Learning, OpenCV

  1. 1. Computer Vision and Deep Learning with OpenCV 3 – Part I Farshid Pirahansiah
  2. 2. Introduction Video Analysis  VCA: Video content analysis (Video content analytics) is the capability of automatically analyzing video to detect and determine temporal and spatial events.  Entertainment  health-care  Retail  Automotive  Transport  home automation  flame and smoke detection  Safety  security
  3. 3. Introduction Video Analysis II  Motion Detection  Video tracking and ego motion estimation  Based on VCA  Identification  behavior analysis  VCA combined with  video enhancement technologies  video denoising  image stabilization,  unsharp masking  super-resolution
  4. 4. Video Analytic  Action detection (walking & running)  Movement detection  Framework for Behavior Detection based on event using human tracking  Framework for Action Detection based on event  Framework for Event
  5. 5. Video Analysis, Tracking  Motion based multiple object tracking  Kernel-based tracking  mean-shift tracking  Contour tracking  active contours  condensation algorithm (Conditional Density Propagation)
  6. 6. Deep Learning – Resources  OpenCV: Deep Neural Network module  http://docs.opencv.org/3.1.0/d6/d0f/group__dnn. html  http://www.deeplearningbook.org/  http://docs.opencv.org/3.1.0/d5/de7/tutorial_d nn_googlenet.html  http://neuralnetworksanddeeplearning.com/ch ap6.html
  7. 7. Datasets for Computer vision + Deep Learning Google Research: Computer vision + Deep Learning 1. Open Images Dataset 2. YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research October 2016
  8. 8. Datasets for images  Deep learning needs large amount of inputs for training. detecting and classifying objects in static images  Open Images Dataset  automatically caption images  natural language replies in response to shared photos  ~9 million URLs to images  6000 categories  each image has about 8 labels assigned  Inception v3 model
  9. 9. Datasets for Video analysis I 1. video is much more time-consuming to annotate manually than images  video annotation system, which identifies relevant Knowledge Graph topics  video metadata and content analysis  only public videos with more than 1000 views  frequency analysis, automated filtering, verification by human raters  24 top-level verticals 2. video is very computationally expensive to process and store  extracted frame-level features  Inception-V3 image annotation model
  10. 10. Datasets for Video analysis II  YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research  8 million YouTube video URLs (representing over 500,000 hours of video)  4800 Knowledge Graph entities (classes)

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