Computer Vision, Deep Learning, OpenCV
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The document discusses computer vision and deep learning using OpenCV. It introduces video content analysis applications in various domains like entertainment, healthcare, retail, etc. It describes techniques for motion detection, tracking, identification and behavior analysis in video. It also discusses frameworks for action and event detection. Finally, it recommends resources for deep learning using OpenCV and describes large datasets like Open Images and YouTube-8M that are useful for training computer vision and deep learning models on images and video.
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Computer Vision, Deep Learning, OpenCV
- 1. Computer Vision and Deep Learning with OpenCV 3 – Part I Farshid Pirahansiah
- 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. 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. 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. Video Analysis, Tracking Motion based multiple object tracking Kernel-based tracking mean-shift tracking Contour tracking active contours condensation algorithm (Conditional Density Propagation)
- 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. 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. 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. 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. 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)