基于IMPSiamCAR孪生网络无人机目标跟踪算法

针对无人机进行目标跟踪时，目标存在尺度变化大、易受遮挡、相似物干扰等问题，在SiamCAR的基础上提出IMPSiamCAR算法。该算法使用改进的ResNet50网络提取目标特征，引入通道注意力机制使模型学习不同通道的语义信息，按特征的重要程度为通道分配不同的权重，使算法能更加关注存在跟踪目标的区域；再将融合后的目标特征送入区域回归网络进行正负样本分类、中心度计算及边界框回归；最后得到每一帧中目标的位置。在UAV123数据集与OTB100数据集上测试的实验结果表明，提出的算法与对比算法相比，有更高的跟踪精度与成功率，能较好地应对遮挡、相似物干扰、尺度变化等挑战；并且在VOT2018和UAV123数据集上进行实时性测试的结果表明，所提算法可以满足无人机实时性的要求。

Tracking algorithm of unmanned aerial vehicle targets based on impsiamcar for siamese network

In order to resolve the problems of large scale change, susceptibility to occlusion and similar object interference when target tracking by UAV, based on SiamCAR, this paper proposed IMPSiamCAR algorithm. The algorithm used the improved ResNet50 network to extract the target features, introduced the channel attention mechanism to make the model learn the semantic information of different channels, and assigned different weights to channels according to the importance of the features, so that the algorithm could pay more attention to the regions where the tracking targets exist. Then it fed the fused target features into the region regression network for positive and negative sample classification, centrality calculation and bounding box regression. Finally, it obtained the position of the target in each frame. Test experimental results of UAV123 dataset and OTB100 dataset show that the proposed algorithm has higher tracking accuracy and success rate compared with the comparison algorithm, and can better deal with challenges such as occlusion, similar object interference as well as scale change. Moreover the results of real-time testing on VOT2018 and UAV123 datasets show that the proposed algorithm can fulfill the UAV real-time request.