TY - GEN
T1 - POD
T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
AU - Koh, Yeong Jun
AU - Jang, Won Dong
AU - Kim, Chang-Su
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055037).
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/12/9
Y1 - 2016/12/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84986269237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986269237&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2016.121
DO - 10.1109/CVPR.2016.121
M3 - Conference contribution
AN - SCOPUS:84986269237
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1068
EP - 1076
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PB - IEEE Computer Society
Y2 - 26 June 2016 through 1 July 2016
ER -