Structural superpixel descriptor for visual tracking

Wenjun Huang, Ruimin Hu, Chao Liang, Weijian Ruan, Bo Luo

Abstract: Object representation is a major component in object tracking, however, most conventional patch-based methods just simply decompose the object into patches with grid or stochastic rectangles. This kind of decomposition ignores the intrinsic structure of object, leading to low discriminative power and weak representation effectiveness when similar objects appear or under background clutters. In this paper, we propose an effective object descriptor based on a hierarchical representation with superpixels for visual tracking, called Structural Superpixel Descriptor (SSD). The proposed SSD not only exploits the superpixels to capture the structural information of object, but also preserves the spatial layout structure among the superpixels inside each target candidate. Moreover, we propose an adaptive patch weighting method based on spatial constraint to alleviate various adverse impacts of background information, making the tracker more robust against background noises. We show that the proposed SSD makes full use of the intrinsic structure inside target candidates. Extensive experiments conducted on various challenging sequences demonstrate that the proposed tracker performs well against state-of-the-art algorithms.