Periodic motion detection and segmentation via approximate sequence alignment
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Proceedings of the IEEE International Conference on Computer Vision |
Pages (from-to) | 816-823 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2005 |
Externally published | Yes |
Event | Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China Duration: 17 Oct 2005 → 20 Oct 2005 |
Conference
Conference | Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 |
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Country | China |
City | Beijing |
Period | 17/10/2005 → 20/10/2005 |
Sponsor | Institute of Electrical and Electronics Engineers, IEEE, IEEE Comput. Soc. Tech. Committee on PAMI |
ID: 302054648