  Recently, the compressive tracking (CT) method has attracted much attention
due to its high efficiency, but it cannot well deal with the large scale target
appearance variations due to its data-independent random projection matrix that
results in less discriminative features. To address this issue, in this paper
we propose an adaptive CT approach, which selects the most discriminative
features to design an effective appearance model. Our method significantly
improves CT in three aspects: Firstly, the most discriminative features are
selected via an online vector boosting method. Secondly, the object
representation is updated in an effective online manner, which preserves the
stable features while filtering out the noisy ones. Finally, a simple and
effective trajectory rectification approach is adopted that can make the
estimated location more accurate. Extensive experiments on the CVPR2013
tracking benchmark demonstrate the superior performance of our algorithm
compared over state-of-the-art tracking algorithms.
