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JOTS: Joint Online Tracking and Segmentation

Title
JOTS: Joint Online Tracking and Segmentation
Author
Yang, Ming-hsuan
Keywords
Labeling; Target tracking; Computational modeling; Image segmentation; Minimization; Motion segmentation
Issue Date
2015-06
Publisher
IEEE
Citation
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 2226-2234
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.
URI
http://ieeexplore.ieee.org/document/7298835/?isnumber=7298593&arnumber=7298835http://hdl.handle.net/20.500.11754/25612
ISBN
978-1-4673-6964-0
ISSN
1063-6919
DOI
10.1109/CVPR.2015.7298835
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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