Robust Object Tracking with Gradient Activation Siamese Network

Deep learning technology has driven continuous advancements in the visual tracking field. In order to overcome various challenges, Siamese-based trackers and Attention-based trackers improve tracking performance by adding or deepening the network structure. However, they also increase training costs and model complexity. Lightweight trackers focus on achieving efficient and real-time tracking with limited computational resources, but these methods often face issues with lower accuracy and robustness in complex scenarios. Our approach – Gradient Activation Siamese Network(Grad-Siam), adds fully connected layers to the backbone of SiamFC to obtain gradient activation information. By using the gradient activation strategy, we dynamically update the feature extraction process of the template and search branches. We trained on GOT-10k and validated on multiple standard test datasets. Experiments show that Grad-Siam balances accuracy and efficiency well, while also enhancing robustness in complex scenarios.