Year: 2025
Type: article
Abstract: Early conflict detection in kindergartens plays a significant role in ensuring a harmonious learning atmosphere and in promoting the social growth of young children. While most previous works have only addressed conflict detection through adults, in this paper, we specifically address conflict detection in kindergartens using deep learning, utilizing both spatial and temporal information to improve performance. The application of deep learning and computer vision in automatically detecting and analyzing early conflicts among young children is discussed in this paper. Using video footage, we leverage state-of-the-art RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facial expressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension or aggression signs. The model is evaluated on real kindergarten video data, with promising conflict detection and classification results. The findings indicate the potential of AI-supported tools in assisting teachers in class management, child behavior monitoring, early intervention mechanisms, and the fostering of a good social environment.
DOI: 10.47344/7x77b619
Author: Dina Kengesbay
Institutions: Shandong University, University of Southern Denmark
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411406011