Periodic Motion Detection and Segmentation via Approximate Sequence Alignment
Ivan Laptev, Serge J. Belongie, Patrick Pérez and Josh Wills
To appear in proc. ICCV 2005, Bijing, China.
Abstract
A method for detecting and segmenting periodic motion is presented.
We exploit periodicity as a cue and detect periodic motion
in complex scenes where common methods for motion segmentation are
likely to fail.
We note that periodic motion detection can be seen as
an approximate case of sequence alignment where an image sequence
is matched to itself over one or more periods of time.
To use this observation, we first consider alignment of two video sequences
obtained by independently moving cameras.
Under assumption of constant translation, the fundamental matrices and
the homographies are shown to be time-linear matrix functions.
These dynamic quantities can be estimated by matching
corresponding space-time points with similar local motion and shape.
For periodic motion, we match corresponding points across periods
and develop a RANSAC procedure to simultaneously estimate the period and the
dynamic geometric transformations between periodic views.
Using this method,
we demonstrate detection and segmentation of human periodic motion
in complex scenes with non-rigid backgrounds, moving camera and motion parallax.
PDF:
(2.2Mb)
Demonstractions:
Segmentation demo (0.5Mb)
Related publications:
(Space-Time Interest Points)
Ivan Laptev