Structured fragment-based object tracking using discrimination, uniqueness, and validity selection

Local features have widely been used in visual tracking to improve robustness in the presence of partial occlusion, deformation, and rotation. In this paper, a local fragment-based object tracking algorithm is proposed. Unlike many existing fragment-based algorithms using all the fragments and allocating the weight to each fragment according to similarity, the proposed algorithm only selects discriminative, unique, and valid fragments for tracking. First, discrimination and uniqueness metric are defined for each local fragment, and an automatic pre-selection mechanism is proposed for all these fragments. Second, a Harris-SIFT filter is used to select the current valid fragments and exclude the occluded or highly deformed fragments. By selecting the discriminative, unique, and valid fragments, these fragments are used to construct a structured description for the object. Finally, the object tracking is performed using the selected fragments combining the displacement and similarity, as well as spatial constraint of the selected fragments. The object template can be updated by fusing feature similarity and structural consistency. The experimental results on a recent OTB 2013 tracking benchmark data set demonstrate that the proposed algorithm can achieve reliable tracking results even in the presence of significant appearance changes, partial occlusion, and similar disturbances.