基于高效注意力和上下文感知的目标跟踪算法

基于匹配思想的孪生网络算法缺乏对目标的整体性感知，容易出现对目标状态估计不够精准和在复杂环境中跟丢的现象。为此，在孪生网络的基础上设计了2个轻量级的模块来实现更精准、更鲁棒的目标跟踪。在提取特征的主干网络之后，嵌入一个高效通道注意力模块，实现高效提取目标特征并增强差异化表示，使网络更注重于目标信息；模板匹配之后的特征通过一个局部上下文感知模块，增强网络对目标的整体感知，以应对跟踪过程中复杂多变的环境；采用Anchor-free的状态估计策略实现对目标的精准估计。实验结果表明:所提算法SiamCC在数据集OTB100、VOT2016和VOT2018上的测试结果均好于DaSiamRPN、ATOM等算法，并且跟踪速度达到了85帧/s。 

Target tracking algorithm based on efficient attention and context awareness

The matching-based Siamese network algorithm often lacks the overall perception of a target, which easily leads to inaccurate target state estimation and target missing in complex environments. Therefore, this paper designs two lightweight modules on the basis of the twin network to achieve more accurate and robust target tracking. An efficient channel attention module is embedded into the backbone network after its construction for feature extraction. Efficient extraction of target features and enhanced differential representation are achieved. so that the network pays more attention to the target information. The features after template matching pass a local context awareness module, thus enhancing the network's overall perception of the target to deal with the complex and changeable environment in the tracking process. The Anchor-free state estimation strategy is used to achieve accurate estimation of the target. Experimental results show that on the datasets OTB100, VOT2016 and VOT2018, SiamCC algorithm outperforms DaSiamRPN algorithms and ATOM algorithm, with the tracking speed reaching 85 frame/s.