【摘要】 针对基于孪生网络的跟踪算法在目标快速移动、发生较大形变、处于复杂背景等情况下容易出现跟踪性能下降的问题，提出一种融合注意力机制与自适应模板更新的目标跟踪算法。跟踪算法以SiamRPN为基础，通过在特征提取网络融合通道注意力机制与空间注意力机制，抑制图像中的干扰信息，补充目标特征在通道空间中的信息，更好地对目标进行定位。将对象在不同时刻的模板，包括初始模板、累积模板和预测模板作为帧残差模块的输入，采用残差学习策略，充分利用初始模板语义信息，自适应更新当前帧所需的模板，减少了跟踪飘移的现象。在OTB100数据集上的实验结果表明，本文跟踪算法与其他跟踪算法相比取得了更高的跟踪成功率和准确率。更多还原

【Abstract】 Aiming at the problem that the tracking algorithm based on twin network is prone to degradation under the condition of fast moving target, large deformation and complex background, a target tracking algorithm based on the integration of attention mechanism and adaptive template updating is proposed. Based on SiamRPN, the tracking algorithm combines the channel attention mechanism and the spatial attention mechanism in the feature extraction network to suppress the interference information in the image, supplement the target feature information in the channel space, and better locate the target. The template of the object at different times, including the initial template, the accumulated template and the predicted template, is taken as the input of the residual module. The residual learning strategy is adopted to make full use of the semantic information of the initial template and adaptively update the template needed for the current frame, which reduces the phenomenon of tracking drift. Experimental results on the OTB100 dataset show that the proposed tracking algorithm achieves higher tracking success rate and accuracy compared with other tracking algorithms.更多还原