Video Based Children's Social Behavior Classification in Peer-Play Scenarios

Abstract

Labeling children's play behavior is an important process in children's social behavior analysis which is traditionally done by experienced coders. With the growing volume of data, automatic methods for labeling are increasingly required. This paper presents a novel method to label children's social behavior automatically in peer-play scenarios based on visual attention and mutual interaction computation. In this method, the discrete distribution of children's visual attention is computed based on face pose estimation. Then, the mutual interaction among children is calculated by "Attention Processes", which are continuous periods of time during which a certain child pays attention to the same target. After that, "Solitary Feature" and "Group Feature" are extracted by the mutual interaction of children and children's play behaviors will be classified into 3 types ("Solitary Play", "Parallel Play" and "Group Play") by these two kinds of features. Finally, this method is evaluated by a dataset of children's peer-play scenarios and the results show this method has a good performance in our dataset.

Vocal turn-taking patterns in groups of children performing collaborative tasks: an exploratory study

“…In order to develop a social robot that interacts with groups of children, we need to gain a better understanding of the situated nonverbal cues expressed by these children, and automatically process and understand these cues. Although some previous studies have explored conversational analysis of collaboration or social interaction among children [12,13,14,15], patterns of vocal interaction in a small group of children involving collaborative tasks remain unexplored. The lack of research in this area might be due to the fact that performing research on children raises many challenges and consequently, there are not many corpora available for the research.…”