TY - CHAP

T1 - Computer Vision for Analyzing Children’s Lived Experiences

AU - Zahra, Anam

AU - Martin, Pierre Etienne

AU - Bohn, Manuel

AU - Haun, Daniel

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Children’s social and physical environment plays a significant role in their cognitive development. Therefore, children’s lived experiences are important to developmental psychologists. The traditional way of studying everyday experiences has become a bottleneck because it relies on short recordings and manual coding. Designing a non-invasive child-friendly recording setup and automating the coding process can potentially improve the research standards by allowing researchers to study longer and more diverse aspects of experience. We leverage modern computer vision tools and techniques to address this problem. We present a simple and non-invasive video recording setup and collect egocentric data from children. We test the state-of-the-art object detectors and observe that egocentric videos from children are a challenging problem, indicated by the low mean Average Precision of state-of-the-art. The performance of these object detectors can be improved through fine-tuning. Once accurate object detection has been achieved, other questions, such as human-object interaction and scene understanding, can be answered. Developing an automatic processing pipeline may provide an important tool for developmental psychologists to study variation in everyday experience.

AB - Children’s social and physical environment plays a significant role in their cognitive development. Therefore, children’s lived experiences are important to developmental psychologists. The traditional way of studying everyday experiences has become a bottleneck because it relies on short recordings and manual coding. Designing a non-invasive child-friendly recording setup and automating the coding process can potentially improve the research standards by allowing researchers to study longer and more diverse aspects of experience. We leverage modern computer vision tools and techniques to address this problem. We present a simple and non-invasive video recording setup and collect egocentric data from children. We test the state-of-the-art object detectors and observe that egocentric videos from children are a challenging problem, indicated by the low mean Average Precision of state-of-the-art. The performance of these object detectors can be improved through fine-tuning. Once accurate object detection has been achieved, other questions, such as human-object interaction and scene understanding, can be answered. Developing an automatic processing pipeline may provide an important tool for developmental psychologists to study variation in everyday experience.

KW - Cognitive development

KW - Egocentric computer vision

KW - Object detection

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85192209453&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/97d56cad-1c25-33ed-9eb1-b84d4d430b5a/

U2 - 10.1007/978-3-031-47724-9_25

DO - 10.1007/978-3-031-47724-9_25

M3 - Article in conference proceedings

AN - SCOPUS:85192209453

SN - 978-3-031-47723-2

T3 - Lecture Notes in Networks and Systems

SP - 376

EP - 383

BT - Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2

A2 - Arai, Kohei

PB - Springer Science and Business Media Deutschland GmbH

CY - Cham

T2 - Intelligent Systems Conference - IntelliSys 2023

Y2 - 7 September 2023 through 8 September 2023

ER -