The Siamese network-based tracker calculates object templates and search images independently, and the template features are not updated online when performing object tracking. Adapting to interference scenarios with performance-guaranteed tracking accuracy when background clutter, illumination variation or partial occlusion occurs in the search area is a challenging task. To effectively address the issue with the abovementioned interference and to improve location accuracy, this paper devises a Siamese residual attentional aggregation network framework for self-adaptive feature implicit updating. First, SiamRAAN introduces Self-RAAN into the backbone network by applying residual self-attention to extract effective objective features. Then, we introduce Cross-RAAN to update the template features online by focusing on the high-relevance parts in the feature extraction process of both the object template and search image. Finally, a multilevel feature fusion module is introduced to fuse the RAAN-enhanced feature information and improve the network’s ability to perceive key features. Extensive experiments conducted on benchmark datasets (GOT-10K, LaSOT, OTB-50, OTB-100 and UAV123) demonstrated that our SiamRAAN delivers excellent performance and runs at 51 FPS in various challenging object tracking tasks. Code is available at https://github.com/MallowYi/SiamRAAN.

基于Siamese网络的跟踪器独立计算对象模板和搜索图像，并且在执行对象跟踪时模板特征不会在线更新。当搜索区域出现背景杂乱、光照变化或部分遮挡时，以保证性能的跟踪精度适应干扰场景是一项具有挑战性的任务。为了有效解决上述干扰问题并提高定位精度，本文设计了一种用于自适应特征隐式更新的Siamese残差注意力聚合网络框架。首先，SiamRAAN将Self-RAAN引入主干网络，通过应用残差自注意力来提取有效的客观特征。然后，我们引入 Cross-RAAN 来在线更新模板特征，重点关注对象模板和搜索图像的特征提取过程中的高相关性部分。最后，引入多级特征融合模块来融合RAAN增强的特征信息，提高网络感知关键特征的能力。在基准数据集（GOT-10K、LaSOT、OTB-50、OTB-100 和 UAV123）上进行的大量实验表明，我们的 SiamRAAN 在各种具有挑战性的对象跟踪任务中具有出色的性能并以 51 FPS 的速度运行。代码可在 https://github.com/MallowYi/SiamRAAN 获取。