  Object segmentation in infant's egocentric videos is a fundamental step in
studying how children perceive objects in early stages of development. From the
computer vision perspective, object segmentation in such videos pose quite a
few challenges because the child's view is unfocused, often with large head
movements, effecting in sudden changes in the child's point of view which leads
to frequent change in object properties such as size, shape and illumination.
In this paper, we develop a semi-automated, domain specific, method to address
these concerns and facilitate the object annotation process for cognitive
scientists allowing them to select and monitor the object under segmentation.
The method starts with an annotation from the user of the desired object and
employs graph cut segmentation and optical flow computation to predict the
object mask for subsequent video frames automatically. To maintain accuracy, we
use domain specific heuristic rules to re-initialize the program with new user
input whenever object properties change dramatically. The evaluations
demonstrate the high speed and accuracy of the presented method for object
segmentation in voluminous egocentric videos. We apply the proposed method to
investigate potential patterns in object distribution in child's view at
progressive ages.
