Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/628373
Title: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
Authors: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
subject: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
Year: 2016
Publisher: IEEE
Abstract: Recent advances in online visual tracking focus on designing part-based model to handle the deformation and occlusion challenges. However, previous methods usually consider only the pairwise structural dependences of target parts in two consecutive frames rather than the higher order constraints in multiple frames, making them less effective in handling large deformation and occlusion challenges. This paper describes a new and efficient method for online deformable object tracking. Different from most existing methods, this paper exploits higher order structural dependences of different parts of the tracking target in multiple consecutive frames. We construct a structure-aware hyper-graph to capture such higher order dependences, and solve the tracking problem by searching dense subgraphs on it. Furthermore, we also describe a new evaluating data set for online deformable object tracking (the Deform-SOT data set), which includes 50 challenging sequences with full annotations that represent realistic tracking challenges, such as large deformations and severe occlusions. The experimental result of the proposed method shows considerable improvement in performance over the state-of-the-art tracking methods.
URI: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
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Title: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
Authors: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
subject: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
Year: 2016
Publisher: IEEE
Abstract: Recent advances in online visual tracking focus on designing part-based model to handle the deformation and occlusion challenges. However, previous methods usually consider only the pairwise structural dependences of target parts in two consecutive frames rather than the higher order constraints in multiple frames, making them less effective in handling large deformation and occlusion challenges. This paper describes a new and efficient method for online deformable object tracking. Different from most existing methods, this paper exploits higher order structural dependences of different parts of the tracking target in multiple consecutive frames. We construct a structure-aware hyper-graph to capture such higher order dependences, and solve the tracking problem by searching dense subgraphs on it. Furthermore, we also describe a new evaluating data set for online deformable object tracking (the Deform-SOT data set), which includes 50 challenging sequences with full annotations that represent realistic tracking challenges, such as large deformations and severe occlusions. The experimental result of the proposed method shows considerable improvement in performance over the state-of-the-art tracking methods.
URI: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7471473.pdf6.5 MBAdobe PDFThumbnail
Preview File
Title: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
Authors: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
subject: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
Year: 2016
Publisher: IEEE
Abstract: Recent advances in online visual tracking focus on designing part-based model to handle the deformation and occlusion challenges. However, previous methods usually consider only the pairwise structural dependences of target parts in two consecutive frames rather than the higher order constraints in multiple frames, making them less effective in handling large deformation and occlusion challenges. This paper describes a new and efficient method for online deformable object tracking. Different from most existing methods, this paper exploits higher order structural dependences of different parts of the tracking target in multiple consecutive frames. We construct a structure-aware hyper-graph to capture such higher order dependences, and solve the tracking problem by searching dense subgraphs on it. Furthermore, we also describe a new evaluating data set for online deformable object tracking (the Deform-SOT data set), which includes 50 challenging sequences with full annotations that represent realistic tracking challenges, such as large deformations and severe occlusions. The experimental result of the proposed method shows considerable improvement in performance over the state-of-the-art tracking methods.
URI: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
volume: 25
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7471473.pdf6.5 MBAdobe PDFThumbnail
Preview File