  Visual attention, derived from cognitive neuroscience, facilitates human
perception on the most pertinent subset of the sensory data. Recently,
significant efforts have been made to exploit attention schemes to advance
computer vision systems. For visual tracking, it is often challenging to track
target objects undergoing large appearance changes. Attention maps facilitate
visual tracking by selectively paying attention to temporal robust features.
Existing tracking-by-detection approaches mainly use additional attention
modules to generate feature weights as the classifiers are not equipped with
such mechanisms. In this paper, we propose a reciprocative learning algorithm
to exploit visual attention for training deep classifiers. The proposed
algorithm consists of feed-forward and backward operations to generate
attention maps, which serve as regularization terms coupled with the original
classification loss function for training. The deep classifier learns to attend
to the regions of target objects robust to appearance changes. Extensive
experiments on large-scale benchmark datasets show that the proposed attentive
tracking method performs favorably against the state-of-the-art approaches.
