摘要
随着卫星视频成像技术的显著进步,卫星视频中的目标跟踪任务引起了越来越多研究人员的关注.然而之前的研究大多通过全局注意力机制获得空间信息,这种方法使得模型关注背景部分从而忽略目标;而且只利用视频帧中目标的空间信息,目标定位不准确.本文对现有的孪生网络目标跟踪模型SiamCAR进行改进,提出时空孪生网络模型Siam-STM.具体来说,本文提出基于注意力机制的空间信息感知模块,聚合图像中的上下文信息并增强卫星视频中小目标特征的辨别力;为了利用视频帧之间的时间信息,本文还提出时间信息感知模块对视频中当前帧和历史帧进行融合,从而学习到不同时刻目标的位置信息,更好的关注目标轨迹,缓解相似干扰物的影响.此外,为了缓解卫星视频中常见的遮挡影响,本文在卡尔曼滤波器的基础上引入线性拟合方法,进而提出一种运动估计机制,可以有效地建模目标的运动特征进而在目标被遮挡时准确定位目标.在SatSOT数据集上通过与现有先进模型的实验对比验证了Siam-STM的有效性.
With the significant progress of satellite video imaging technology,object tracking in satellite videos has attracted more and more researchers’attention.However,most of the previous research obtains spatial information through the global attention mechanism,which makes the model focus on the background part and thus ignore the object;moreover,only spatial information of the object in the video frames is utilized,resulting in inaccurate object localization.In this study,we improve the existing Siamese network object tracking model SiamCAR and a spatio-temporal Siamese network Siam-STM.Specifically,we propose a spatial information perception module based on the attention mechanism,which aggregates the contextual information in the images and enhances the discriminative capability of small object features in the satellite videos;to utilize the temporal information across video frames,a temporal information perception module is proposed to fuse the current frame with the historical frames,enabling the position information of the object across time to be learned,the object’s trajectory to be better tracked,and the interference from similar objects to be mitigated.In addition,to mitigate the effects of occlusion in satellite videos,this study introduces a linear fitting method based on the Kalman filter and then proposes a motion estimation mechanism.This mechanism can effectively model the motion characteristics of the object,allowing accurate localization even during occlusions.The effectiveness of SiamSTM is verified by comparing it with state-of-the-art models on the SatSOT dataset.
作者
顾权炜
王洁
GU Quan-Wei;WANG Jie(College of Computer Science,Beijing University of Technology,Beijing 100124,China)
出处
《计算机系统应用》
2025年第8期53-61,共9页
Computer Systems & Applications
关键词
孪生网络
卫星视频目标跟踪
空间信息
时间信息
运动估计机制
Siamese network
object tracking in satellite videos
spatial information
temporal information
motion estimation
