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Summarizing Motion In Video Sequences

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The goal was to summarize the motion of foreground objects in video taken by a ... Meant to imitate the work of the photographers Marey and Bragaglia. ... – PowerPoint PPT presentation

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Title: Summarizing Motion In Video Sequences


1
Summarizing Motion In Video Sequences
  • Kevin Forbes
  • May 27, 2004

2
Project Goal
  • The goal was to summarize the motion of
    foreground objects in video taken by a fixed
    camera against a static background.
  • This was done using two major techniques
    pseudo-photographic image processing, and action
    line rendering.

3
Pseudo-photographic Image Processing
  • Meant to imitate the work of the photographers
    Marey and Bragaglia.
  • Three techniques Multiple Exposure, Long
    Exposure, and Motion History Imaging.

4
Multiple Exposure
  • Based on foreground segmentation (threshold
    difference)
  • Layer-by-time
  • Variable spacing

5
Long Exposure
  • Requires segmentation (implemented using
    alpha-blitting)
  • Layer-by-time (again)
  • Variable exposure time, falloff

6
Multiple Long Exposures
7
Motion History Images
As seen in J. Davis and A. Bobick. The
representation and recognition of action using
temporal templates, 1997.
8
Action Line Rendering
  • Used in comics and technical illustration
  • Formulate as a search problem

9
Line Model
  • Straight line segments
  • Four Parameters Angle, Radius, End Points
  • Groups of parallel lines will be favoured by the
    search method

10
Input Images and Line Fitting
11
Line Scoring
  • Monotonism
  • Timeliness
  • Temporal Length
  • Spatial Length
  • Smoothness

12
Search Strategy
  • Candidate Generation
  • Regular sampling along angle and radius
  • Score all monotonic regions, add positive scores
    to candidate pool
  • Candidate Selection
  • Sort pool by score
  • Add N candidates, starting with highest scoring,
    deferring on intersection

13
Results Good!
14
Results Bad and Ugly!
15
Conclusions
  • The pseudo-photographic techniques are limited
    by their reliance upon segmentation.
  • Using HDR images, you could more closely emulate
    a camera, but the sequence would need to have
    controlled lighting
  • Action line fitting shows promise for
    translational motions, but a more robust line
    model should be used to produce better results
  • Possible line models groups of parallel lines,
    articulated line segments, curves, arc segments
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