Learning Attentions: Residual Attentional Siamese Network
for High Performance Online Visual Tracking
Abstract
Offline training for object tracking has recently shown
great potentials in balancing tracking accuracy and speed.
However, it is still difficult to adapt an offline trained model
to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance
object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt
the model without updating the model online. In particular,
by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting
problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation
of representation learning and discriminator learning. The
proposed deep architecture is trained from end to end and
takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results
on two latest benchmarks, OTB-2015 and VOT2017, show
that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second