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24/10/02 AutoArch Overview
Computer Vision and Media Group:
Selected Previous Work
David Gibson, Neill Campbell
Colin Dalton
Department of Computer Science
University of Bristol
24/10/02 AutoArch Overview
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
24/10/02 AutoArch Overview
24/10/02 AutoArch Overview
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!
24/10/02 AutoArch Overview
24/10/02 AutoArch Overview
24/10/02 AutoArch Overview
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
24/10/02 AutoArch Overview
24/10/02 AutoArch Overview
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!
24/10/02 AutoArch Overview
24/10/02 AutoArch Overview
AutoArch: The Automatic Archiving of Wildlife Film
Footage
David Gibson, Neill Campbell
David Tweed, Sarah Porter
Department of Computer Science
University of Bristol
24/10/02 AutoArch Overview
Motivation
• BBC Natural History Unit
• Manual archiving/meta data generation
• Reuse problematic– Inefficient/time consuming– Expensive– Limited access
• Obvious need to automate
24/10/02 AutoArch Overview
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
24/10/02 AutoArch Overview
System Overview
ShotSegmentation
VisualSummarisation
MotionAnalysis
Colour/TextureAnalysis
Meta data extraction algorithms
Catalogue Entry
Visualisation based algorithms
Visualisation and Searching
24/10/02 AutoArch Overview
Video Segmentation
24/10/02 AutoArch Overview
Visual Summarisation
• Key frame extraction
24/10/02 AutoArch Overview
Visual Summarisation Tree
Ent
ire
shot
Level of detail
24/10/02 AutoArch Overview
Visual Searching
• Layered 2D representation
of high D clip space
24/10/02 AutoArch Overview
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
24/10/02 AutoArch Overview
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 -8.27
pan/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
24/10/02 AutoArch Overview
Camera Zoom
BCD0113.15_0067.epslines = 142, curls = 1, shorts = 7long lines = 134, mode = 340.00, mean = 227.24, std = 128.76
zoom 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
24/10/02 AutoArch Overview
Tracking Failure
This could be an interestingevent in its self: flocking,herding, close up of lots ofactivity, shot grouping, etc.
24/10/02 AutoArch Overview
Event/Area of InterestDetection
24/10/02 AutoArch Overview
Frequency Analysis:Gait Detection
FFT
After trajectory segmentation
24/10/02 AutoArch Overview
Foreground/BackgroundExtraction
Feature space #1
Feat
ure
spac
e #2
Foregroundmodel
Backgroundmodel
Which pixelsare foreground?
24/10/02 AutoArch Overview
Animal Identification
Give models a name:
= cheetah
= elephant
= zebra
= lion
24/10/02 AutoArch Overview
Some Problems
• Noise in images
• Noise in measurements
• Camouflage
• Occlusion
• Answer: Need higher level models
• See next few slides
24/10/02 AutoArch Overview
Model Based Tracking
24/10/02 AutoArch Overview
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
24/10/02 AutoArch Overview
Multiple Object Tracking
24/10/02 AutoArch Overview
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
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