Abstract
A primary object discovery (POD) algorithm for a video sequence is proposed in this work, which is capable of discovering a primary object, as well as identifying noisy frames that do not contain the object. First, we generate object proposals for each frame. Then, we bisect each proposal into foreground and background regions, and extract features from each region. By superposing the foreground and background features, we build the object recurrence model, the background model, and the primary object model. We develop an iterative scheme to refine each model evolutionarily using the information in the other models. Finally, using the evolved primary object model, we select candidate proposals and locate the bounding box of a primary object by merging the proposals selectively. Experimental results on a challenging dataset demonstrate that the proposed POD algorithm extracts primary objects accurately and robustly.
Original language | English |
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Title of host publication | 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
Publisher | IEEE Computer Society |
Pages | 1068-1076 |
Number of pages | 9 |
Volume | 2016-January |
ISBN (Electronic) | 9781467388511 |
Publication status | Published - 2016 |
Event | 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1 |
Other
Other | 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
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Country | United States |
City | Las Vegas |
Period | 16/6/26 → 16/7/1 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition