【摘要】 针对复杂背景干扰下的红外目标跟踪，提出融合并行混合注意力机制的候选区域孪生网络跟踪算法。并行混合注意力机制以并行的方式计算空间注意力和通道注意力特征图，然后将空间和通道注意力特征图通过维度扩展转化为与输入特征图相同的维度，并将扩展后的两类注意力特征图进行逐元素相乘以有效聚合多注意力权重，从而获取混合注意力特征图。同时，为能够根据混合注意力权重动态调整原始特征图中对应元素的权重，将混合注意力特征图和输入特征图进行逐元素相乘。在候选区域孪生特征提取网络中融入并行混合注意力机制并对地/空背景下红外飞机目标进行跟踪，实验结果表明，相对于SiamRPN、Siam BAN、Mixformer，提出算法的成功率和精度分别提高了15.5%、1.8%、8.5%和20.1%、9.3%、7.7%，跟踪速度达到201帧/s，能够有效实现复杂背景干扰下的红外目标跟踪且有较好的实时性。更多还原

【Abstract】 A candidate region twin-network tracking algorithm with a parallel hybrid attention mechanism is proposed to solve the problem of infrared target tracking under complex background interference. The parallel hybrid attention mechanism calculates spatial-and channel-attention feature maps in parallel. Subsequently,the spatial-and channel-attention feature maps are transformed into the same dimensions as the input feature map through dimensional expansion. Then, the extended spatial-and channel-attention feature maps are multiplied element-wise to effectively aggregate multiple attention weights, thereby obtaining a hybrid attention feature map. To dynamically adjust the weights of the corresponding elements in the original feature map based on the hybrid attention weights, the hybrid attention feature map and the input feature map are multiplied element-wise. A parallel hybrid attention mechanism is integrated into SiamRPN, and infrared aircraft targets under a ground/air background are tracked. Experimental results show that compared with SiamRPN, SiamBAN, and Mixformer, the success rate and accuracy of the proposed algorithm are improved by 15.5%, 1.8%, 8.5% and 20.1%, 9.3%, and 7.7%, respectively, whereas its tracking speed reaches 201frames/s. The proposed method effectively realizes infrared target tracking under complex background interference and exhibits favorable real-time performance.更多还原