Detecting social conflicts in kindergartens using deep learning and computer vision

Kengesbay D.

Detecting social conflicts in kindergartens using deep learning and computer vision

dc.contributor.author	Kengesbay D.
dc.date.accessioned	2026-03-13T10:43:13Z
dc.date.available	2026-03-13T10:43:13Z
dc.date.issued	2025
dc.description.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-theart 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
dc.identifier.citation	Kengesbay D / Detecting social conflicts in kindergartens using deep learning and computer vision / SDU University / Faculty of Engineering and Natural Sciences
dc.identifier.uri	https://repository.sdu.edu.kz/handle/123456789/2534
dc.language.iso	en
dc.publisher	SDU University
dc.subject	social conflict detection
dc.subject	computer vision
dc.subject	early childhood education
dc.subject	AI in education
dc.title	Detecting social conflicts in kindergartens using deep learning and computer vision
dc.type	Thesis