Robust Visual Tracking With a Novel Segmented Fine-grained Regularization

Most of the Siamese network tracking algorithms use L2 regularization in the training stage, while ignoring the hierarchy and characteristic of the network architecture. As a result, such trackers have poor robustness. With this insight, we propose a segmented fine-grained regularization tracking (SFGRT) algorithm, which divides the regularization of Siamese network into three fine-grained levels, namely filter level, channel level and shape level. Then we creatively build a segmented fine-grained regularization model that constructs penalty functions based on group lasso, which combines with different levels of granularity to improve generalization ability and robustness. In addition, aiming at the imbalance of gradient magnitude of each penalty function, our approach constructs a gradient self-balancing optimization function to adaptively optimize the coefficients of each penalty function. Finally, ablation study on VOT2019 show that compared with the baseline algorithm SiamRPN++, our approach achieves relative gains of 7.1% and 1.7% in terms of robustness and expected average overlap (EAO) metrics, respectively. It means that the robustness of our tracker is significantly enhanced over baseline tracker since the smaller the robustness metrics, the better. Extensive experiments based on VOT2018, VOT2019, UAV123 and LaSOT show that the proposed algorithm has better robustness and tracking performance than related state-of-the-art methods. © 2023 Science Press. All rights reserved.