CSE Colloquia Series-David Crandall

Oct 18
11:00 a.m.
Lopata Hall, Room 101

Egocentric computer vision, for fun and for science


The typical datasets we use to train and test computer vision algorithms consist of millions of consumer-style photos. But this imagery is significantly different from what humans actually see as they go about their daily lives. Low-cost, light wearable cameras (like GoPro) now make it possible to record people's lives from a first-person, "egocentric" perspective that approximates their actual fields of view. What new applications are possible with these devices? How can computer vision contribute to and benefit from this embodied perspective on the world? What could mining datasets of first-person imagery reveal about ourselves and about the world in general? In this talk, I'll describe recent work investigating these questions, focusing on two lines of work on egocentric imagery as examples. The first is for consumer applications, where our goal is to develop automated classifiers to help organize first-person images across several dimensions. The second is an interdisciplinary project using computer vision with wearable cameras to study parent-child interactions in order to better understand child learning. Despite the different goals, these applications share common themes of robustly recognizing image content in noisy, highly dynamic, unstructured imagery.


David Crandall is an Associate Professor and Director of Graduate Studies in the Department of Computer Science at Indiana University. He is also a member of the programs in Informatics, Cognitive Science, and Data Science, and co-directs the Center for Algorithms and Machine Learning. He received the Ph.D. in computer science from Cornell University in 2008 and the M.S. and B.S. degrees in computer science and engineering from the Pennsylvania State University in 2001. He was a Postdoctoral Research Associate at Cornell from 2008-2010, and a Senior Research Scientist with Eastman Kodak Company from 2001-2003. He is an Associate Editor of the

IEEE Transactions on Pattern Analysis and Machine Intelligence and the IEEE Transactions on Multimedia. He received an NSF CAREER award (2013), a Google Faculty Research Award (2014), best paper awards or nominations at CVPR, CHI, ICDL, ICCV, and WWW, an Indiana University Trustees Teaching Award (2017), and is an IU Grant Thorton Scholar (2019).

Organizer / Host: Ayan Chakrabarti