Paralleled attention modules and adaptive focal loss for Siamese visual tracking

Recently, Siamese‐based trackers have drawn amounts of attention in visual tracking field because of their excellent performance on different tracking benchmarks. However, most Siamese‐based trackers encounter difficulties under circumstances such as similar objects interference and background clutters. Besides, there exists an extreme foreground–background data imbalance that weakens the performance during training but few loss functions pay attention to it. The authors intend to address the issues mentioned above by introducing a module named paralleled spatial and channel attention (PSCA) and adaptive focal loss (AFL). Firstly, paralleled spatial and channel attention is proposed to enhance the extracted features and eliminate the noise information from both spatial and channel aspects. Secondly, adaptive focal loss is proposed as the loss function to make the model focus on hard samples that contribute more to training process. Finally, paralleled spatial and channel attention and modified ResNet are combined for extracting more powerful features. Experimental results show that the authors' method achieves outstanding performance in multiple benchmarks while keeping a beyond‐real‐time frame rate.