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

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.