Learning online structural appearance model for robust object tracking

The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance variations. Existing tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance changes. However, slight inaccuracy of tracking results can degrade the appearance model. In this paper, we propose a robust tracking method by evaluating an online structural appearance model based on local sparse coding and online metric learning. Our appearance model employs pooling of structural features over the local sparse codes of an object region to obtain a middle-level object representation. Tracking is then formulated by seeking for the most similar candidate within a Bayesian inference framework where the distance metric for similarity measurement is learned in an online manner to match the varying object appearance. Both qualitative and quantitative evaluations on various challenging image sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.