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Discovering the physical parts of an articulated object class from multiple videos

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http://ieeexplore.ieee.org/document/7780453/
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages714-723
Number of pages10
ISBN (Electronic) 978-1-4673-8851-1
ISBN (Print)978-1-4673-8852-8
DOIs
Publication statusPublished - 12 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016
http://cvpr2016.thecvf.com/

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2016
CountryUnited States
CityLas Vegas
Period26/06/161/07/16
Internet address

Abstract

We propose a motion-based method to discover the physical parts of an articulated object class (e.g. head/torso/leg of a horse) from multiple videos. The key is to find object regions that exhibit consistent motion relative to the rest of the object, across multiple videos. We can then learn a location model for the parts and segment them accurately in the individual videos using an energy function that also enforces temporal and spatial consistency in part motion. Unlike our approach, traditional methods for motion segmentation or non-rigid structure from motion operate on one video at a time. Hence they cannot discover a part unless it displays independent motion in that particular video. We evaluate our method on a new dataset of 32 videos of tigers and horses, where we significantly outperform a recent motion segmentation method on the task of part discovery (obtaining roughly twice the accuracy).

Event

29th IEEE Conference on Computer Vision and Pattern Recognition

26/06/161/07/16

Las Vegas, United States

Event: Conference

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