Computer Vision for Analyzing Children’s Lived Experiences

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.