计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 236-243.doi: 10.11896/jsjkx.220600037
杨岚岚, 王文琪, 王福田
YANG Lan-lan, WANG Wen-qi, WANG Fu-tian
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。
中图分类号:
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