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The offline Siamese tracking methods have recently attracted much attention due to their impressive performance on speed and accuracy. However, they ignore the target and background information during tracking, thus lacking sufficient ...

• A feature refining module for obtaining target-special features.
• Online discriminative learning for background suppression.
• Sample equilibrium strategy for alleviating the imbalance of online samples.
• Dynamic templates for ...

Converged Ethernet employs Priority-based Flow Control (PFC) to provide a lossless network. However, issues caused by PFC, including victim flow, congestion spreading, and deadlock, impede its large-scale deployment in production systems. The fine-grained ...

In recent years, Siamese-based trackers have shown remarkable improvement in visual tracking. The general trends are interested in making deeper and more complicated networks to pursue higher accuracy. However, these advances result in ...

• Investigate and demonstrate the hard-to-compress problem of deep trackers.
• ...

Siamese-based trackers suffer from over-parameterization and online learning limitations, making it difficult to balance high performance and real-time execution when deployed on resource-constrained devices. In this paper, we propose ...

In recent years, considering a balanced accuracy and efficiency, Fully-Convolutional Siamese network (SiamFC) is widely used in the field of visual tracking. Although SiamFC has achieved great success, it is still frustrated in discrimination ...

Most visual tracking algorithms based on Siamese network adopt a fixed template in the whole tracking process, which sacrifices the available target appearance information. To address this issue, we propose a dual-stream collaborative ...

Existing discriminative correlation filters suffer from the defects of potential spatial distractors and the degradation of appearance model caused by hard-temporal correlation. Aiming at this issue, a robust tracker which combines the adaptive ...