计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 269-277.doi: 10.11896/jsjkx.210400121
董虎胜1,2, 钟珊3, 杨元峰1,2, 孙逊1,2, 龚声蓉3
DONG Hu-sheng1,2, ZHONG Shan3, YANG Yuan-feng1,2, SUN Xun1,2, GONG Sheng-rong3
摘要: 在对行人重识别的研究中,联合使用从图像中提取的全身与局部特征已经成为当前的主流方法。但是许多基于深度学习的重识别模型在提取局部特征时忽略了它们在空间上的相互联系,当不同行人具有局部相似的外观时,这些局部特征的辨别能力会受到很大影响。针对该问题,提出了一种学习多粒度区域相关特征的行人重识别方法。该方法在对骨干网络提取的卷积特征张量作不同粒度的区域划分后,设计了区域相关子网络模块来学习融入空间结构关系的各局部区域特征。在区域相关子网络模块中,为了赋予局部特征与其他区域相关联的空间结构信息,综合利用了平均池化运算的空间保持能力与最大池化运算的性能优势。通过对当前特征和其他各区域的局部特征进行联合处理,使各局部特征间产生很强的空间相关性,提升了特征判别能力。在区域相关子网络模块的设计上,采用了与深度残差网络相同的短路连接结构,使得网络更易于优化。最后,由全身特征与使用区域相关子网络增强后的各局部区域特征联合实现行人重识别。Market-1501,CUHK03,DukeMTMC-reID 3个公开数据集上的实验结果表明,所提算法取得了优于当前主流算法的行人身份匹配准确率,具有非常优秀的重识别性能。
中图分类号:
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