  Convolutional Siamese neural networks have been recently used to track
objects using deep features. Siamese architecture can achieve real time speed,
however it is still difficult to find a Siamese architecture that maintains the
generalization capability, high accuracy and speed while decreasing the number
of shared parameters especially when it is very deep. Furthermore, a
conventional Siamese architecture usually processes one local neighborhood at a
time, which makes the appearance model local and non-robust to appearance
changes.
  To overcome these two problems, this paper proposes DensSiam, a novel
convolutional Siamese architecture, which uses the concept of dense layers and
connects each dense layer to all layers in a feed-forward fashion with a
similarity-learning function. DensSiam also includes a Self-Attention mechanism
to force the network to pay more attention to the non-local features during
offline training. Extensive experiments are performed on four tracking
benchmarks: OTB2013 and OTB2015 for validation set; and VOT2015, VOT2016 and
VOT2017 for testing set. The obtained results show that DensSiam achieves
superior results on these benchmarks compared to other current state-of-the-art
methods.
