【摘要】 针对单目标跟踪中，因形变、运动模糊、遮挡以及背景干扰导致的跟踪框精度下降问题，特别是在背景干扰下易出现跟踪跳变及漂移问题，提出了一种结合运动信息和双重注意力机制的两阶段跟踪算法.第一阶段，使用带有双重注意力机制的SiamCAR跟踪器对当前帧的目标进行粗定位；第二阶段，利用像素级相似度运算构建边界框精细化模块，在低延迟情况下学习目标的细微特征以提升跟踪精度，并将基于外观特征得到的跟踪框与目标的运动轨迹信息相融合，以改善跟踪漂移及跳变问题. OTB100数据集上的实验结果表明，跟踪框的成功率和精度相比原来分别提高了4.6%和2.8%，在背景干扰下的成功率达到了69.6%.更多还原

【Abstract】 In single-object tracking, the accuracy of the tracking bounding box is often compromised by factors such as deformation, motion blur, occlusion, and background interference. In particular, background interference frequently leads to tracking hopping and drift. To mitigate these issues, a two-stage tracking algorithm that integrated motion information with a dual-attention mechanism was proposed. In the first stage, a SiamCAR tracker with a dualattention mechanism was employed to coarsely locate the target in the current frame. In the second stage, a refinement module of the bounding box was constructed using pixel-level similarity computations to learn the subtle features of the target under low-latency conditions, thereby enhancing the tracking accuracy. Finally, the tracking box obtained based on appearance features was fused with the target’s motion trajectory information to mitigate tracking drift and hopping. Experimental results on the OTB100 dataset indicate that the success rate and accuracy of the tracking box have improved by 4. 6% and 2. 8%, respectively, compared to the original. The success rate in the presence of background interference has reached 69. 6%.更多还原