A Study of Visual Attention on Gestures and Motion during Infancy
In recent years, understanding development of children’s visual attention with the help of computer vision techniques have been promising. Many approaches have been tried to understand what are the factors that generate attention in infants. Analyzing videos taken from different perspectives have been increasingly useful in such studies as they provide new insights. Nevertheless, analyzing these videos frame by frame is time consuming and unmanageable. Moreover, it is difficult for humans to assess all of the parameters that impact child's visual attention.
In this thesis, we have proposed a tool for extracting and analyzing the motions from videos of child-parent toy play. We have focused primarily on the third perspective videos. The approach first extracts dense trajectories from these videos, and then uses unsupervised clustering to group the trajectories into multiple groups. These groups are then analyzed to explore potential correlations between the motions of the parents and the attention of the child. The proposed tool will enable researchers to look into unknown patterns that might contribute into the development of children’s visual attention by analyzing child-parent toy play videos.