Visual engagement estimation and gaze tracking on child interactions

Μπενέτος, Νικόλαος; Benetos, Nikolaos

dc.contributor.author	Μπενέτος, Νικόλαος	el
dc.contributor.author	Benetos, Nikolaos	en
dc.date.accessioned	2025-03-21T09:16:53Z
dc.date.available	2025-03-21T09:16:53Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61398
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29094
dc.rights	Default License
dc.subject	Μηχανική Μάθηση	el
dc.subject	Στοχοπροσήλωση	el
dc.subject	Παρακολούθηση Βλέμματος	el
dc.subject	Όραση Υπλογιστών	el
dc.subject	Νευρωνικά Δίκτυα	el
dc.title	Visual engagement estimation and gaze tracking on child interactions	en
dc.title	Εκτίμηση στοχοπροσήλωσης και βλέμματος σε βίντεο αλληλεπιδράσεων παιδιών	el
heal.type	bachelorThesis
heal.classification	Όραση Υπολογιστών	el
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-24
heal.abstract	Computer Vision is a field of Artificial Intelligence that aims in enabling computer systems to derive important information from visual inputs, such as images and videos, in order to take actions or make recommendations based on them. It has had applications on a variety of areas and industries, like transportation, education, healthcare, security systems and smartphones, for that reason it's advancement is deemed necessary. Another area where computer vision has submerged is in studying children interactions through the analysis of video data, which is crucial for understanding how children develop social, emotional, and cognitive skills, particularly in early childhood. Key to these interactions is the child’s engagement, which is a valid indicator of how focused the child is during its interaction and how different approaches change its behaviour. Engagement from a visual perspective is often measured through behaviors like gaze, which indicates attention and focus. Understanding child engagement is crucial for assessing their learning, social interactions, and cognitive development. Gaze estimation, as a key aspect of non-verbal communication, plays a pivotal role in interpreting attention levels and identifying atypical behavioral patterns, such as those seen in children with Autism Spectrum Disorder (ASD). This thesis leverages state-of-the-art computer vision techniques to automatically track and assess children's gaze and engagement levels. In addition to that, it studies the effectiveness of Temporal Segment Networks (TSN), in improving the capabilities of the proposed models in those tasks. We create and implement frameworks for both the engagement estimation and gaze tracking and test them with visual data using only RGB data as input, from really challenging datasets that capture children interacting freely in different environments. Finally, we create a different kind of network that combines the engagement and gaze estimation networks, while using the TSN method, into a new architecture to further improve the engagement estimation. By utilizing machine learning models, the thesis aims to provide an accurate and scalable solution for real-time analysis of these behaviors. The findings contribute to the growing body of research in early childhood development, offering potential tools for professionals to better understand and support children’s developmental needs, particularly in identifying early signs of disorders such as ASD.	el
heal.advisorName	Μαραγκός, Πέτρος	el
heal.committeeMemberName	Ποταμιάνος, Γεράσιμος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Σημάτων, Ελέγχου και Ρομποτικής	el
heal.academicPublisherID	ntua
heal.numberOfPages	124 σ.	el
heal.fullTextAvailability	false