多信息融合的卫星视频单目标跟踪

Single Target Tracking of Satellite Video Based on Multi-information Fusion

针对卫星视频中存在目标特征信息少、前景背景对比性低等问题,在SiamCAR的基础上提出了一种融合运动信息和注意力机制的目标跟踪方法;首先引入运动激励模块和通道注意力模块以增强目标特征提取信息;然后将相邻帧作为新模板添加到网络里形成三重网络补充模板信息;最后加入卡尔曼滤波算法进行目标轨迹预测,将预测模板添加到网络中形成四重网络增加目标的运动信息;选取SatSOT卫星视频数据集中的10组数据进行测试,实验结果表明与SiamCAR网络相比,改进算法的跟踪准确率和成功率分别提升了6%和6.2%.

Aiming at the problems of less target feature information and low contrast between foreground and background in satellite video,this study proposes a target tracking method integrating motion information and attention mechanism based on SiamCAR.First,the motion excitation and channel attention modules are introduced to enhance the target feature extraction information.Then,adjacent frames are regarded as new templates and added to the network to form a triple network and supplement template information.Finally,the Kalman filter algorithm is added to predict the target’s trajectory,and a prediction template is introduced to the network to construct a quadruple network and increase the motion information of the target.In addition,10 sets of data in the SatSOT satellite video data set are selected for testing.The experimental results show that compared with those of the SiamCAR network,the tracking accuracy and success rate of the improved algorithm are increased by 6%and 6.2%,respectively.