Data Set Hannah and Her Sisters was the basis for a recently released computer vision dataset.

Published on December 20th, 2013 | by Travis Korte


Researchers Build Computer Vision Dataset Based on Woody Allen Film

Automatic face-tracking in television, film and other complex video has emerged in recent years as an important topic in computer vision research. It underlies numerous applications, such as finding an individual within a collection of security footage, or tracking changes in viewer response while a certain TV character is on screen. But creators of face-tracking algorithms often have a hard time assessing the performance of their software because they lack an accurate benchmark dataset for comparison. Computer scientists from France’s Rennes Research and Innovation Center hope to solve that problem with Hannah, a face-tracking dataset extracted manually from over 150,000 frames in the 1986 Woody Allen film, Hannah and Her Sisters. 

Faces are tagged from the moment they enter the frame until the moment they leave, except when more than half of their face is occluded. Each of the film’s 52 named characters is tagged along with the face tracks. Audio annotation, which is considerably easier to collect, was added to the dataset as well.

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About the Author

Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.

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