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Multimed Model. 2016;9516:738-751. Epub 2016 Jan 3.

Robust Object Tracking Using Valid Fragments Selection.

Author information

1
Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China; State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
2
Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
3
Schepens Eye Research Institute, Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA.

Abstract

Local features are widely used in visual tracking to improve robustness in cases of partial occlusion, deformation and rotation. This paper proposes a local fragment-based object tracking algorithm. Unlike many existing fragment-based algorithms that allocate the weights to each fragment, this method firstly defines discrimination and uniqueness for local fragment, and builds an automatic pre-selection of useful fragments for tracking. Then, a Harris-SIFT filter is used to choose the current valid fragments, excluding occluded or highly deformed fragments. Based on those valid fragments, fragment-based color histogram provides a structured and effective description for the object. Finally, the object is tracked using a valid fragment template combining the displacement constraint and similarity of each valid fragment. The object template is updated by fusing feature similarity and valid fragments, which is scale-adaptive and robust to partial occlusion. The experimental results show that the proposed algorithm is accurate and robust in challenging scenarios.

KEYWORDS:

Fragments-based tracking; Harris-SIFT filter; Structured fragments; Template update; Valid fragment selection

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