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عنوان: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
پدیدآورنده: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
کلید واژه ها: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
تاریخ انتشار: 2016
ناشر: IEEE
چکیده: 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.
آدرس: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
دوره: 25
شماره: 8
مجموعه(های):2016

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
7471473.pdf6.5 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
پدیدآورنده: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
کلید واژه ها: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
تاریخ انتشار: 2016
ناشر: IEEE
چکیده: 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.
آدرس: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
دوره: 25
شماره: 8
مجموعه(های):2016

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
7471473.pdf6.5 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph
پدیدآورنده: Dawei Du;Honggang Qi;Wenbo Li;Longyin Wen;Qingming Huang;Siwei Lyu
کلید واژه ها: Online tracking|deformable object tracking|structure-aware hyper-graph|dense subgraph searching|part-based model
تاریخ انتشار: 2016
ناشر: IEEE
چکیده: 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.
آدرس: http://localhost/handle/Hannan/152280
http://localhost/handle/Hannan/628373
ISSN: 1057-7149
1941-0042
دوره: 25
شماره: 8
مجموعه(های):2016

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
7471473.pdf6.5 MBAdobe PDFتصویر
مشاهده فایل