Paper Info
Title
Periodic Motion Detection and Segmentation via Approximate Sequence Alignment
Abstract
A method for defecting and segmenting periodic motion is presented. We exploit periodicity as a cite and detect periodic motion in complex scenes where common methods for rnotion 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.
Year
DOI
Venue
2005
10.1109/ICCV.2005.188
ICCV
Keywords
Field
DocType
motion parallax,similar local motion,complex scene,periodic view,human periodic motion,periodic motion detection,approximate sequence alignment,corresponding space-time point,corresponding point,common method,periodic motion,space time,image segmentation,sequence alignment
Structure from motion,Periodic function,Computer vision,Motion field,Quarter-pixel motion,Scale-space segmentation,Computer science,Image segmentation,Artificial intelligence,Motion estimation,Match moving
Conference
ISSN
ISBN
Citations 
1550-5499
0-7695-2334-X-01
50
PageRank 
References 
Authors
1.66
25
4
Name
Order
Citations
PageRank
Ivan Laptev18560416.71
Serge J. Belongie2125121010.13
Patrick Pérez36529391.34
Josh Wills41536.53