Siamese anchor-free network based on dual attention mechanism for visual tracking

Jie Cao, J. Kang, Bin Dai, Xiaoxu Li
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Abstract

In order to improve the ability of extracting discriminant features and add valid location information, a new Siamese anchor-free network based on dual attention mechanism is proposed—DASN. The method introduces the coordinate channel attention module and the spatial attention module to get the context features that contain the precise location information of the target. DASN can enhance the feature extraction by modeling the dependency between channels and positions, thus achieve the improvement of the accuracy of classification and locating and the robustness of tracking. Experimental results show that the proposed method achieves the performance improvement on datasets VOT2018, OTB-2013 and OTB-2015, and also meets the real-time requirements.
基于双注意机制的Siamese无锚网络视觉跟踪
为了提高识别特征的提取能力和添加有效位置信息的能力,提出了一种基于双注意机制的暹罗无锚网络- dasn。该方法通过引入坐标通道注意模块和空间注意模块来获取包含目标精确位置信息的上下文特征。DASN可以通过对通道和位置之间的依赖关系进行建模来增强特征提取,从而提高分类定位的精度和跟踪的鲁棒性。实验结果表明,该方法在VOT2018、OTB-2013和OTB-2015数据集上实现了性能提升,同时满足实时性要求。
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