Segmentation of periodically moving objects

We present a new approach for the identification and segmentation of objects undergoing periodic motion. Our method uses a combination of maximum likelihood estimation of the period, and segments moving objects using correlation of image segments over an estimated period of interest. Correlation provides the best locations of the moving objects in each frame. Segmentation tree provides the image segments at multiple resolutions. We ensure that children regions and their parent regions have the same period estimates. We show results of testing our method on real videos.