Zhangdong Wang1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2931-2946, 2022, DOI:10.32604/cmc.2022.028896
Abstract Visual object tracking is a hot topic in recent years. In the meanwhile, Siamese networks have attracted extensive attention in this field because of its balanced precision and speed. However, most of the Siamese network methods can only distinguish foreground from the non-semantic background. The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC) can achieve higher precision under interferences, but the tracking accuracy is still not ideal, especially in the environment with more target interferences, dim light, and shadows. In this paper, we propose criss-cross attentional Siamese networks for object tracking (SiamCC). To solve the imbalance between foreground… More >
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