基于深度学习的飞机目标跟踪应用研究

本文针对飞机目标，提出了基于多域网络(MDNet)的改进网络用于飞机跟踪的快速深度学习(FDLAT)跟踪网络，使用迁移学习弥补目标跟踪的小样本集缺陷。卷积层作为特征提取层，全连接层作为目标和背景的分类层，采用特定的飞机数据集来更新网络参数。训练完成之后，结合回归模型，采用简单的线性更新对飞机进行跟踪，算法实现了飞机旋转、相似目标、模糊目标、复杂环境、尺度变换、目标遮挡以及形态变换等复杂状态的鲁棒跟踪，速度达到平均20.36 f/s，在ILSVRC2015飞机检测数据集上成功率均值达到0.592，基本满足飞机实时跟踪。

Application of aircraft target tracking based on deep learning

In this paper, based on muti-domain network (MDNet), fast deep learning for aircraft tracking (FDLAT) algorithm is proposed to track aircraft target. This algorithm uses feature-based transfer learning to make up the inferiority of small sample sets, uses specific data sets to update parameters of convolutional layers and fully connected layers, and use it to distinguish aircraft from background. After building the training model, we put the aircraft video sets into the model and tracked the aircraft using regression model and a simple line on-line update, to increase the speed while ensuring the accuracy. This algorithm achieves robust tracking for aircraft in rotation, similar targets, fuzzy targets, complex environment, scale transformation, target occlusion, morphological transformation and other complex states, and runs at a speed of 20.36 frames with the overlap reached 0.592 in the ILSVRC2015 detection sets of aircraft, basically meets the real-time application requirement of aircraft tracking.