基于高秩特征和位置注意力的RGBT目标跟踪  

RGBT Object Tracking Based on High Rank Feature and Position Attention

作　　者：杨岚岚 王文琪 王福田[1] YANG Lan-lan;WANG Wen-qi;WANG Fu-tian(School of Computer Science and Technology,Anhui University,Hefei 230000,China)

机构地区：[1]安徽大学计算机科学与技术学院,合肥230000

出　　处：《计算机科学》2022年第12期236-243,共8页Computer Science

摘　　要：RGBT目标跟踪利用可见光(RGB)与热红外(T)两种不同模态的优势来解决单一模态目标跟踪中常见的模态受限问题,以此提升复杂环境下的目标跟踪性能。在RGBT目标跟踪算法中,精准定位目标位置和有效融合两种模态都是非常重要的问题。为了达到精准定位目标以及有效融合两种模态的目的,提出了一种探索高秩的特征图以及引入位置注意力来进行RGBT目标跟踪的新方法。该方法首先根据主干网络的深层与浅层的特征,使用位置注意力来关注目标的位置信息,接着通过探索两种模态融合前的高秩特征图,关注特征的重要性,以指导模态特征融合。为了关注目标位置信息,在行和列上使用平均池化操作。对于高秩特征指导模块,文中根据特征图的秩来指导特征图的融合。并且,为了去除冗余和噪声,实现更加鲁棒的特征表达,直接删除了秩小的特征图。在两个RGBT跟踪基准数据集上的实验结果表明,与其他RGBT目标跟踪方法相比,所提方法在准确度和成功率上取得了更好的跟踪结果。RGBT target tracking uses the advantages of two different modes of visible light(RGB)and thermal infrared(T)to solve the common modal limitation problem in single mode target tracking,so as to improve the performance of target tracking in complex environment.In the RGBT object tracking algorithm,the precise location of the object and the effective fusion of the two modalities are very important issues.In order to accurately locate the object and effectively fuse the two modalities,this paper proposes a new method to explore high-rank feature maps and introduce position attention for RGBT object tracking.The method first uses location attention to focus on the location information of the object according to the deep and shallow features of the backbone network,and then focuses on the importance of the features by exploring the high-rank feature maps before the fusion of the two modalities to guide the modal features fusion.In order to focus on the object location information,this paper uses the average pooling operation on the rows and columns.For the high-rank feature guidance module,this paper guides the fusion of feature maps according to the rank of the feature maps.In order to remove redundancy and noise and achieve more robust feature expression,the feature graph with small rank is deleted directly.Experimental results on two RGBT tracking benchmark data sets show that compared with other RGBT target tracking methods,the proposed method achieves better tracking results in accuracy and success rate.

关 键 词：RGBT目标跟踪 高秩特征图 目标位置信息 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]