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所属单位：机电学院

发表刊物：IEEE Trans Multimedia

摘要：RGB-D video segmentation is important for many applications, including scene understanding, object tracking, and robotic grasping. However, to segment RGB-D frames over a long video sequence into globally consistent segmentation is still a challenging problem. Current methods often lose pixel correspondences between frames under occlusion and, thus, fail to generate consistent and continuous segmentation results. To address this problem, we propose a novel spatiotemporal RGB-D video segmentation framework that automatically segments and tracks objects with continuity and consistency over time. Our approach first produces consistent segments in some keyframes by region clustering, and then propagates the segmentation result to a whole video sequence via a mask propagation scheme in bilateral space. Instead of exploiting local optical, flow information to establish correspondences between adjacent frames, we leverage scale-invariant feature transform (SIFT) flow and bilateral representation to solve inconsistency under occlusion. Moreover, our method automatically extracts multiple objects of interest and tracks them without any user input hint. A variety of experiments demonstrates effectiveness and robustness of our proposed method. © 1999-2012 IEEE.

ISSN号：1520-9210

是否译文：否

发表时间：2018-03-01

合写作者：谢乾,Remil, Oussama,Guo, Yanwen,Wang, Meng,汪俊

通讯作者：汪俊,魏明强