计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 267-271.doi: 10.11896/jsjkx.181001861
包宗铭1, 龚声蓉1,2, 钟珊1,2, 燕然1, 戴兴华1
BAO Zong-ming1, GONG Sheng-rong1,2, ZHONG Shan1,2, YAN Ran1, DAI Xing-hua1
摘要： 在跨摄像头的行人再识别任务中,光照、视角以及遮挡物等成像因素导致行人外观在不同视角下呈现出巨大变化,这使得对目标行人的再识别工作变得十分困难。利用重排序算法虽然可以在一定程度上提高行人再识别的准确率,但增加了时间成本和人力成本,且容易引入新的噪声。为此,文中提出了一种基于双向KNN排序优化的行人再识别算法。首先,采用预训练加微调的策略来提取行人的深度特征;然后,利用XQDA和KISSME两种度量学习方法来比较特征间的距离,计算初始排名;最后,根据查询图像和候选图像间的双向KNN关系计算Jaccard距离,并将其与初始距离加权求和作为重排序的参照,计算出新的排名。在CUHK03,Market1501和PRW 3个数据集上的实验表明,文中提出的重排序算法在Rank1和mAP两个评价指标上分别获得了12.2%和13.4%的提升。实验数据充分说明,基于双向KNN排序优化的行人再识别算法可以有效降低重排序时引入噪声的概率,从而提高行人再识别的准确率。
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