SiamRPN++ is an advanced Siamese visual tracking method that improves performance by leveraging deep networks like ResNet. The core issue with traditional Siamese trackers is the lack of strict translation invariance, which limits their ability to use deep features effectively. The authors propose a spatial-aware sampling strategy to overcome this limitation, enabling the use of deeper networks and achieving significant performance gains. They also introduce a new model architecture that performs layer-wise and depth-wise aggregations, enhancing accuracy while reducing model size. The proposed tracker, SiamRPN++, achieves state-of-the-art results on five major tracking benchmarks: OTB2015, VOT2018, UAV123, LaSOT, and TrackingNet. The model is efficient, running at 35 FPS, and a faster variant using MobileNet achieves 70 FPS. The method also includes a depth-wise cross-correlation layer, which reduces computational cost and improves model stability. Experimental results show that SiamRPN++ outperforms existing methods in terms of accuracy, robustness, and efficiency. The model's performance is validated across various tracking scenarios, demonstrating its effectiveness and generalization capability.SiamRPN++ is an advanced Siamese visual tracking method that improves performance by leveraging deep networks like ResNet. The core issue with traditional Siamese trackers is the lack of strict translation invariance, which limits their ability to use deep features effectively. The authors propose a spatial-aware sampling strategy to overcome this limitation, enabling the use of deeper networks and achieving significant performance gains. They also introduce a new model architecture that performs layer-wise and depth-wise aggregations, enhancing accuracy while reducing model size. The proposed tracker, SiamRPN++, achieves state-of-the-art results on five major tracking benchmarks: OTB2015, VOT2018, UAV123, LaSOT, and TrackingNet. The model is efficient, running at 35 FPS, and a faster variant using MobileNet achieves 70 FPS. The method also includes a depth-wise cross-correlation layer, which reduces computational cost and improves model stability. Experimental results show that SiamRPN++ outperforms existing methods in terms of accuracy, robustness, and efficiency. The model's performance is validated across various tracking scenarios, demonstrating its effectiveness and generalization capability.