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Trackers based on Siamese networks show great potential in tracking accuracy and speed. However, it is still challenging to adapt offline training model to online tracking. In this paper, a Siamese based tracker (SCRPN-CISA) is proposed, which integrates three attention mechanisms and a novel Cascaded Region Proposal Networks (RPN) architecture, for improving the feature extraction ability, adaptability and discrimination ability in complex scenes. Firstly, the deep network VGG-Net-D is adopted as the backbone network in the Siamese framework to increase the feature extraction capability. Then, a Channel-Interconnection-Spatial Attention module is constructed to enhance the adaptive and discriminative capability of the model. Next, a Deconvolution Adjust Block is built to fusion cross-layer features. Finally, a Three-Layer Cascaded RPN is conceived to acquire the foreground-background classification and bounding box regression by correlation calculation, and moreover, a proposal region screening strategy is presented to obtain more accurate tracking results. Experiments on OTB-2015, UAV123, VOT2016, and VOT2019 benchmarks demonstrate that, the proposed tracker (SCRPN-CISA) achieves competitive performance compared with the state-of-the-art trackers.