Most current tracking approaches utilize only one type of feature to represent the target and learn the appearance model of the target just by using the current frame or a few recent ones. The limited representation of one single type of feature might not represent the target well. What's more, the appearance model learning from the current frame or a few recent ones is intolerant of abrupt appearance changes in short time intervals. These two factors might cause the track's failure. To overcome these two limitations, in this paper, we apply the Augmented Kernel Matrix (AKM) classification to combine two complementary features, pixel intensity and LBP (Local Binary Pattern) features, to enrich the target's representation. Meanwhile, we employ the AKM clustering to group the tracking results into a few aspects. And then, the representative patches are selected and added into the training set to learn the appearance model. This makes the appearance model cover more aspects of the target appearance and more robust to abrupt appearance changes. Experiments compared with several state-of-the-art methods on challenging sequences demonstrate the effectiveness and robustness of the proposed algorithm. 
Robust visual tracking via augmented kernel SVM