1 / 31

Computer Vision and Media Group: Selected Previous Work

Computer Vision and Media Group: Selected Previous Work. David Gibson, Neill Campbell Colin Dalton Department of Computer Science University of Bristol. Duck: The Automatic Generation of 3D Models. Generating 3D computer models is difficult Put object on turntable

kovit
Download Presentation

Computer Vision and Media Group: Selected Previous Work

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computer Vision and Media Group:Selected Previous Work David Gibson, Neill Campbell Colin Dalton Department of Computer Science University of Bristol AutoArch Overview

  2. Duck: The AutomaticGeneration of 3D Models • Generating 3D computer models is difficult • Put object on turntable • Take 8 pictures of it from different angles • Crank the handle… • No skilled user or expensive equipment • Make avatars by spinning person on chair AutoArch Overview

  3. AutoArch Overview

  4. Cog and Stepper • Automatically inject ‘life’ into computer animations • 3D swathe through 4D space time • Where space is 3D computer model • Or just to make things look strange! AutoArch Overview

  5. AutoArch Overview

  6. AutoArch Overview

  7. Casablanca: Motion Ripper • Computer animation driven by film • Animator labels a small number of points • System then tracks these points over all frames • Motions are extracted and used to drive animation AutoArch Overview

  8. AutoArch Overview

  9. Laughing ManMotion Ripper Part 2 • Automatic video creation • Points are marked and tracked • System learns the motions • System generates new motions which are different but ‘correct’ • Forever! AutoArch Overview

  10. AutoArch Overview

  11. AutoArch: The Automatic Archiving of Wildlife Film Footage David Gibson, Neill Campbell David Tweed, Sarah Porter Department of Computer Science University of Bristol AutoArch Overview

  12. Motivation • BBC Natural History Unit • Manual archiving/meta data generation • Reuse problematic • Inefficient/time consuming • Expensive • Limited access • Obvious need to automate AutoArch Overview

  13. Objectives • Generate efficient visual representations • Video segmentation • Visual browsing/summarisation • Visual searching • Generate as much meta data automatically • Camera motions/effects • Scene structure • Scene content AutoArch Overview

  14. Visualisation and Searching Visualisation based algorithms Shot Segmentation Visual Summarisation Motion Analysis Colour/Texture Analysis Meta data extraction algorithms Catalogue Entry System Overview AutoArch Overview

  15. Video Segmentation AutoArch Overview

  16. Visual Summarisation • Key frame extraction AutoArch Overview

  17. Entire shot Visual Summarisation Tree Level of detail AutoArch Overview

  18. Visual Searching • Layered 2D representation of high D clip space AutoArch Overview

  19. Motion Analysis using point tracking • Camera Motion Estimation • Event/Area of Interest Detection • Gait Analysis • Foreground/Background Separation • Combine with Colour and Texture for Classification • See cheetah track avi AutoArch Overview

  20. Camera Pan BCD0111.09_0085.epslines = 47, curls = 98, shorts = 5long lines = 47, mode = 95.00, mean = 95.21, std = 4.15zoom centre = (603.01, 63.65), val = -0.2356zoom residual per line = 22.92zoom residual #2 per line = 28.92Average line vector: 109.94 -8.27pan/tilt angle: 94.30, vector: (109.94 -8.27)pan/tilt residual per line = 21.67pan/tilt residual #2 per line = 33.38percentage of lines within 5% of mode: 89.36 AutoArch Overview

  21. Camera Zoom BCD0113.15_0067.epslines = 142, curls = 1, shorts = 7long lines = 134, mode = 340.00, mean = 227.24, std = 128.76zoom centre = (182.97, 55.52), val = 0.2063zoom residual per line = 4.86zoom residual #2 per line = 6.90Average line vector: -3.81 17.28pan/tilt angle: 347.57, vector: (-3.81 17.28)pan/tilt residual per line = 13.85pan/tilt residual #2 per line = 16.13percentage of lines within 5% of mode: 17.16 AutoArch Overview

  22. Tracking Failure This could be an interesting event in its self: flocking, herding, close up of lots of activity, shot grouping, etc. AutoArch Overview

  23. Event/Area of InterestDetection AutoArch Overview

  24. Frequency Analysis:Gait Detection After trajectory segmentation FFT AutoArch Overview

  25. Foreground model Feature space #2 Background model Feature space #1 Foreground/BackgroundExtraction Which pixels are foreground? AutoArch Overview

  26. Animal Identification Give models a name: = zebra = cheetah = lion = elephant AutoArch Overview

  27. Some Problems • Noise in images • Noise in measurements • Camouflage • Occlusion • Answer: Need higher level models • See next few slides AutoArch Overview

  28. Model Based Tracking AutoArch Overview

  29. Lion Tracking • Synchronise horse model with lion points • Move and deform horse model to lion points • See avi • To do: Improve spatial deformation, especially for legs, using colour and texture AutoArch Overview

  30. Multiple Object Tracking AutoArch Overview

  31. Conclusions • Visualisation is very powerful • Combined with text is even better! • Assists searching and communication • Lots of meta data can be auto generated • Assists archiving • Help to prioritise manual archiving • Can be applied to any visual media AutoArch Overview

More Related