Visual Tracking via Nonlocal Similarity Learning

Either global (e.g., intensity histograms and coefficients of sparse representation) or local (e.g., scale-invariant feature transform and histogram of oriented gradient) feature representations have been widely exploited for visual tracking. However, most of these representations describe a target appearance with a fixed spatial grid layout without considering the interactions between different grids, and hence may adversely affect their performance when the target appearance suffers from large-scale pose variations. In this paper, we learn a similarity function that considers the interactions of features in the grids not only from the same spatial positions, but also from different positions, thereby taking charge of the nonlocal information of the target appearances to effectively handle the significant appearance variations. Specifically, we explore the polynomial kernel feature map to characterize the nonlocal similarity information of all pairs of grids among the target and its background samples, and combine these feature maps as the target representations. Moveover, we learn a linear logistic regression classifier with online update to separate the target from its local background, and integrate this classifier into a particle filtering tracking framework. Extensive experimental results on the CVPR2013 tracking benchmark demonstrate the proposed approach performs favorably against some representative tracking algorithms.