基于注意力特征融合的无人机多目标跟踪算法

随着无人机技术的不断发展,无人机多目标跟踪已成为无人机应用的关键技术之一.针对无人机视频中的复杂背景干扰、遮挡、视点高度和角度多变等问题,提出一种基于注意力特征融合的无人机多目标跟踪算法.首先,将改进的卷积注意力模块引入残差网络,建立三元组注意力特征提取网络;其次,在特征金字塔网络的结构上加入新的特征融合通道,设计多尺度特征融合模块,增强模型对多尺度目标的特征表达能力;最后,根据目标的重识别特征匹配与检测框匹配得到目标轨迹.仿真实验结果表明,该算法可有效提升无人机多目标跟踪的精度,具有较好的鲁棒性.

UAV multi-target tracking algorithm based on attention feature fusion

With the development of UAV technology, multi-target tracking of UAV video has become one of the key technologies in the application of UAVs. Aiming at the problems of complex background interference, occlusion, variable viewpoint of height and angle in UAV multi-target tracking video, a multi-target tracking algorithm based on attention feature fusion is proposed. Firstly, the improved convolution attention module is introduced into the residual network, and a three-tuple attention feature extraction network is established. Then, a new feature fusion channel is added to the structure of the feature pyramid network, and a multi-scale feature fusion module is designed to enhance the model''s ability to express the features of multi-scale targets. Finally, the target trajectory is obtained by target re-identification feature matching and bounding box matching. The simulation results show that the algorithm effectively improves the accuracy and robustness of the UAV multi-target tracking.