  In this paper, we propose a robust tracking method based on the collaboration
of a generative model and a discriminative classifier, where features are
learned by shallow and deep architectures, respectively. For the generative
model, we introduce a block-based incremental learning scheme, in which a local
binary mask is constructed to deal with occlusion. The similarity degrees
between the local patches and their corresponding subspace are integrated to
formulate a more accurate global appearance model. In the discriminative model,
we exploit the advances of deep learning architectures to learn generic
features which are robust to both background clutters and foreground appearance
variations. To this end, we first construct a discriminative training set from
auxiliary video sequences. A deep classification neural network is then trained
offline on this training set. Through online fine-tuning, both the hierarchical
feature extractor and the classifier can be adapted to the appearance change of
the target for effective online tracking. The collaboration of these two models
achieves a good balance in handling occlusion and target appearance change,
which are two contradictory challenging factors in visual tracking. Both
quantitative and qualitative evaluations against several state-of-the-art
algorithms on challenging image sequences demonstrate the accuracy and the
robustness of the proposed tracker.
