2015
DOI: 10.1016/j.image.2015.06.001
|View full text |Cite
|
Sign up to set email alerts
|

Geometric structure-constraint tracking with confident parts

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2016
2016
2019
2019

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(4 citation statements)
references
References 30 publications
0
4
0
Order By: Relevance
“…To address this problem, Cai et al [1] model the superpixels together with their relationships as an undirected graph, and formulate the target tracking as a graph matching problem. Xie et al [19] employ a minimum spanning tree to model the geometric structure constraint between superpixels, and infer the target state by voting from confident parts of the target based on appearance similarity and structural constraint. In addition to leveraging the spatial relationships between local parts, Li et al [5] construct a relational hypergraph, which models the high-order relationships among multiple local parts across the temporal domain, and identify temporally coherent parts for the target representation.…”
Section: Bounding Box Levelmentioning
confidence: 99%
See 3 more Smart Citations
“…To address this problem, Cai et al [1] model the superpixels together with their relationships as an undirected graph, and formulate the target tracking as a graph matching problem. Xie et al [19] employ a minimum spanning tree to model the geometric structure constraint between superpixels, and infer the target state by voting from confident parts of the target based on appearance similarity and structural constraint. In addition to leveraging the spatial relationships between local parts, Li et al [5] construct a relational hypergraph, which models the high-order relationships among multiple local parts across the temporal domain, and identify temporally coherent parts for the target representation.…”
Section: Bounding Box Levelmentioning
confidence: 99%
“…The first term measures the probability that the superpixel c I k belongs to the target, and the second term measures the similarity between the two superpixels. Then similar to [19], we get a smoother score map D M i ðIÞ by convolving the score map D M i ðIÞ with a filter:…”
Section: Object State Inferencementioning
confidence: 99%
See 2 more Smart Citations