基于像素分类的多尺度无人机航拍目标旋转跟踪算法

针对无人机（UAV）跟踪过程中垂直跟踪框在处理尺度变化、相似物体和纵横比变化时限制了跟踪精度提升的问题，提出一种基于像素分类的多尺度UAV航拍目标旋转跟踪算法。首先，设计MS-ResNet以提取目标多尺度特征；然后，在具有正交特性的多通道响应图上设计像素二分类模块，从而进一步精确细化分类和回归分支的结果；同时，为了提高像素分类精度，使用并行通道空间注意力（scSE）模块在空间域和通道域上筛选目标特征；最后，在像素分类基础上生成贴合目标实际大小的旋转跟踪框，从而避免正样本受到污染。实验结果表明：所提算法在无人机跟踪数据集UAV123上的成功率和准确率分别为60.7%和79.5%、与孪生区域建议跟踪网络（SiamRPN）相比，成功率与准确率分别提升了5个百分点、2.7个百分点，同时速度为67.5 FPS，满足实时要求。所提算法具有良好的尺度适应能力、辨别能力和鲁棒性，能有效应对UAV跟踪任务。

Pixel classification-based multiscale UAV aerial object rotational tracking algorithm

A pixel classification-based multiscale Unmanned Aerial Vehicle （UAV） aerial object rotational tracking algorithm was proposed for the UAV tracking process， in which the vertical tracking box limited the tracking accuracy when dealing with scale changes， similar objects and aspect ratio changes. Firstly， MS-ResNet （MultiScale ResNet-50） was designed to extract multiscale features of the object. Then， a pixel binary classification module was designed on the multi-channel response map with orthogonal characteristics to further refine the results of classification and regression branches accurately. Meanwhile， to improve the pixel classification accuracy， the concurrent spatial and channel “Squeeze & Excitation” （scSE） module was used to filter the object features in the spatial and channel domains. Finally， a rotational tracking box fitting the actual size of the object was generated based on pixel classification to avoid the contamination of positive samples. Experimental results show that the proposed algorithm has the success rate and precision on the UAV tracking dataset UAV123 of 60.7% and 79.5% respectively， which are 5 percentage points and 2.7 percentage points higher than those of Siamese Region Proposal Network （SiamRPN） respectively， and has the speed reached 67.5 FPS， meeting the real-time requirements. The proposed algorithm has good scale adaptation， discrimination ability and robustness， and can effectively cope with UAV tracking tasks.