Title
Object Detection and Tracking Under Occlusion for Object-Level RGB-D Video Segmentation.
Abstract
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.
Year
DOI
Venue
2018
10.1109/TMM.2017.2751965
IEEE Trans. Multimedia
Keywords
Field
DocType
Video sequences,Image segmentation,Motion segmentation,Clustering algorithms,Object segmentation,Algorithm design and analysis
Computer vision,Object detection,Scale-invariant feature transform,Pattern recognition,Object-class detection,Segmentation,Computer science,Image segmentation,Video tracking,Artificial intelligence,RGB color model,Cluster analysis
Journal
Volume
Issue
ISSN
20
3
1520-9210
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Qian Xie1169.82
Oussama Remil2142.98
Yan-Wen Guo334839.32
Meng Wang43094167.38
Mingqiang Wei512522.66
Jun Wang637247.52