Abstract
We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
| Original language | English |
|---|---|
| Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
| Publisher | IEEE Computer Society |
| Pages | 2226-2234 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781467369640 |
| DOIs | |
| Publication status | Published - 2015 Oct 14 |
| Event | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 2015 Jun 7 → 2015 Jun 12 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 07-12-June-2015 |
| ISSN (Print) | 1063-6919 |
Other
| Other | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 15/6/7 → 15/6/12 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
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JOTS : Joint Online Tracking and Segmentation. / Wen, Longyin; Du, Dawei; Lei, Zhen; Li, Stan Z.; Yang, Ming Hsuan.
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, 2015. p. 2226-2234 7298835 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - JOTS
T2 - Joint Online Tracking and Segmentation
AU - Wen, Longyin
AU - Du, Dawei
AU - Lei, Zhen
AU - Li, Stan Z.
AU - Yang, Ming Hsuan
PY - 2015/10/14
Y1 - 2015/10/14
N2 - We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
AB - We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84959236471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959236471&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2015.7298835
DO - 10.1109/CVPR.2015.7298835
M3 - Conference contribution
AN - SCOPUS:84959236471
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 2226
EP - 2234
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
ER -