Paper Info
Title
Depth Structure Association for RGB-D Multi-target Tracking
Abstract
Multi-target tracking in outdoor scenes plays an important role in many computer vision applications. Most previous work on visual information based multi-target tracking does not incorporate depth information and the absence of depth information often leads to mismatching or tracking failures. In this paper, we propose a Depth Structure Association (DSA) approach for RGB-D data based multi-target tracking. DSA encodes depth information in a chain structure, the structure is used by DSA together with appearance and motion information to address object occlusion issues in outdoor scenes. Additionally, the use of DSA has the advantages of regulating a much smaller solution space, greatly reducing the computational complexity. Experimental results on three datasets demonstrate that our DSA approach can significantly reduce object mismatch and tracking failure for long term occlusions.
Year
DOI
Venue
2014
10.1109/ICPR.2014.711
ICPR
Keywords
Field
DocType
image coding,motion information,outdoor scenes,chain structure,target tracking,object mismatch reduction,depth information encoding,encoding,object occlusion,visual information based multitarget tracking,long term occlusions,depth structure association approach,computational complexity,computer vision,tracking failures,dsa approach,rgb-d multitarget tracking
Computer vision,Multi target tracking,Pattern recognition,Computer science,Tracking system,Artificial intelligence,RGB color model,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
1051-4651
2
0.36
References 
Authors
14
4
Name
Order
Citations
PageRank
Shan Gao180.78
Zhenjun Han217616.40
David Doermann34313312.70
Jianbin Jiao436732.61