计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 142-147.doi: 10.11896/jsjkx.210600198
孟月波1,2, 穆思蓉1, 刘光辉1, 徐胜军1,2, 韩九强1,2
MENG Yue-bo1,2, MU Si-rong1, LIU Guang-hui1, XU Sheng-jun1,2, HAN Jiu-qiang1,2
摘要： 为了提高行人重识别(Re-ID)的准确率和适用性,提出了一种基于向量注意力机制GoogLeNet的Re-ID方法。首先,将3组图像(锚、正、负)输入到GoogLeNet-GMP网络中,获得分段式特征向量。然后,利用空间金字塔池化(Spatial Pyramid Pooling,SPP)对来自不同金字塔等级的特征进行聚合,并引入注意力机制,通过对代表目标视觉信息的多尺度池化区域进行整合,获得多个语义等级上的可区分性特征。同时,将两个不同损失函数的混合形式作为最终损失函数。在Market-15012和Duke-MTMC3数据集上进行实验,结果表明,相比其他优秀方法,所提方法在Rank-1和mAP指标方面表现更优。
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