Distant Viewing TV: An Introduction
15 May 2017
Distant Viewing TV is a new project that uses and develops computational techniques to analyze moving image culture on a large scale. The project includes the development of an open source toolkit for extracting time-coded metadata from moving images and associated audio data. Using these tools, the project analyzes how visual space is used by characters over a set of fourteen sitcoms from the Network Era of American Television (1952-1985), modeling a new mode of cultural analysis. It is currently in active development. More information about the project as a whole can be found on the website distanttv.org.
I will be producing blog posts on this site as a means for documenting the processing of building these tools and analyzing our initial corpus for Distant Viewing TV. We feel that too many projects focus only on the final outcomes, making the process of applied data analysis seem either inaccessible and almost like magic or straightforward and trivial. The truth is that even experts in the field spend hundreds of hours trying a multitude of approaches only to find that the majority of them do not work. We hope that these informal, stream-of-consciousness style pieces documenting the entire process will help to make this more clear.
Currently, the following posts are available. I will update the list as the project progresses.
- Distant Viewing TV: Extract Images
- Distant Viewing TV: Face Detection with OpenFace
- Distant Viewing TV: Shot Detection
- Distant Viewing TV: Initial Training Set
As these pieces focus on the computational aspects of the project they are primarily written by myself in collaboration with Lauren Tilton, the project’s co-PI. The larger project would not be possible without the rest of the project’s core team: Annie Berke, Claudia Calhoun, and Nate Ayers. The Distant Viewing TV team is completed by a fantastic advisory board, consisting of:
- Paul Achter, University of Richmond
- Joel Burges, University of Rochester
- Jeremy Butler, University of Alabama
- Lev Manovich, The Graduate Center at CUNY
- Trevor Muñoz, Univesity of Maryland
- Miriam Posner, UCLA
- Joshua Romphf, University of Rochester
- Holly Rushmeier, Yale University
- Mark Williams, Dartmouth College
We are also grateful for the institutional support of the project through the University of Richmond’s Digital Scholarship Lab. Over time, we expect to produce peer-reviewed scholarship in partnership with our team members and advisors.