Three-stage cascade architecture-based siamese sliding window network algorithm for object tracking

To enhance the correlation of feature information and enrich the pattern of cross-correlation metrics, we propose the Siam ST algorithm, which is based on a three-stage cascade (TSC) architecture. The sliding window is introduced in the last three layers of convolution blocks, which can obtain the global information of images and fully capture the target feature. The TSC structure is developed by using the regional proposal network. It makes the features of the current frame interact with the previous frame. As a result, our method has a high effect of robustness and association features extraction. Therefore, our ablation experiments are conducted on the VOT2016 dataset, and comparison experiments are conducted on four datasets, VOT2018, LaSOT, Tracking Net, and UAV123. Our proposed algorithm demonstrates a significant improvement compared to SiamRPN++ across four datasets.