LAUSR

Siamese networks have gained considerable attention for object tracking due to their balance of speed and accuracy. However, existing Siamese tracking algorithms have been too rigid in their predictions of bounding box tags and lack uncertainty estimation, resulting in poor tracking performance in marine environments, particularly those with waves. To improve the effectiveness of trackers in marine environments, this study proposes a Siamese distillation network. First, to address the issue that the presence of waves and other disturbances may result in target loss or inaccuracy when tracking the target, the concept of a probability distribution of the bounding box is introduced in this study, which transforms the standard Dirac delta distribution of the bounding box into a probability distribution of the bounding box, effectively reducing the impact of interference on tracking performance and improving target location accuracy. Second, we chose ResNet100 as the backbone network to obtain richer features for localization. Finally, this work offers a knowledge distillation approach to further enhance the tracking accuracy and model performance, while considering the impact of the model’s number of parameters and computational amount on tracking performance. This network outperforms most trackers in terms of accuracy, according to extensive experimental results, and performs well on the target tracking benchmark and marine dataset annotated in this study. Specifically, this network achieved the highest accuracy value of 0.612 compared to other Siamese networks, resulting in a 2.5% increase compared to original baseline network. This suggests that the proposed algorithm is practical.