Where's Waldo: Matching People in Images of Crowds
Rahul Garg1        Deva Ramanan2        Steve Seitz1,3        Noah Snavely4
1University of Washington        2University of California at Irvine         3Google Inc.        4Cornell University

We seek to find all instances of a specific person in a community contributed large photo collection. In the above case, trained from a single image at the top-left, our system correctly identifies 4 of the 5 correct matches shown above from a collection of 282 images captures by 82 different people on a single day at Trafalgar Square, London (Images downloaded from Flickr.com)


Given a community-contributed set of photos of a crowded public event, this paper addresses the problem of finding all images of each person in the scene. This problem is very challenging due to large changes in camera viewpoints, severe occlusions, low resolution and photos from tens or hundreds of different photographers. Despite these challenges, the problem is made tractable by exploiting a variety of visual and contextual cues – appearance, timestamps, camera pose and co-occurrence of people. This paper demonstrates an approach that integrates these cues to enable high quality person matching in community photo collections downloaded from Flickr.com

Supplementary Material
Poster [ppt | pdf]
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Acknowledgements: This work was supported in part by National Science Foundation grants IIS-0811878, IIS-0963657 and IIS-0954083, the University of Washington Animation Research Labs, Intel, Microsoft, and Google. We are thankful to Flickr users whose photos we used.