Recently, object trackers based on Siamese deep network have achieved great progress and attract much attention. It
can be helpful to improve the performance of a tracker to mining more effective information from feature maps. In this
paper, we propose a Siamese network tracker with channel attention mechanism based on the SiamCAR tracker. Firstly,
in each residual unit of the ResNet backbone network, each channel feature of the feature map has different importance
for discriminating an object. Channel attention mechanism can be used to calculate the importance of each channel
feature. Secondly, deep features and lower-level features play different roles for tracking too, and attention mechanisms
can be used to fuse the features of each residual stage. On the benchmark datasets, OTB50 and OTB100, the experiments
show that our proposed tracker achieves better tracking performance in AUC and Precision and achieves the real-time
speed of 46 FPS.
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