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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).
Original language | English |
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Title of host publication | Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 714-723 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-4673-8851-1 |
ISBN (Print) | 978-1-4673-8852-8 |
DOIs | |
Publication status | Published - 12 Dec 2016 |
Event | 29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States Duration: 26 Jun 2016 → 1 Jul 2016 http://cvpr2016.thecvf.com/ |
Conference
Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition |
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Abbreviated title | CVPR 2016 |
Country/Territory | United States |
City | Las Vegas |
Period | 26/06/16 → 1/07/16 |
Internet address |
Projects
- 1 Finished