Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph | IEEE Journals & Magazine | IEEE Xplore

Online Deformable Object Tracking Based on Structure-Aware Hyper-Graph


Abstract:

Recent advances in online visual tracking focus on designing part-based model to handle the deformation and occlusion challenges. However, previous methods usually consid...Show More

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.
Published in: IEEE Transactions on Image Processing ( Volume: 25, Issue: 8, August 2016)
Page(s): 3572 - 3584
Date of Publication: 18 May 2016

ISSN Information:

PubMed ID: 27214901

Funding Agency:


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