摘要
目的无监督行人重识别可缓解有监督方法中数据集标注成本高的问题,其中无监督跨域自适应是最常见的行人重识别方案。现有UDA(unsupervised domain adaptive)行人重识别方法在聚类过程中容易引入伪标签噪声,存在对相似人群区分能力差等问题。方法针对上述问题,基于特征具有类内收敛性、类内连续性与类间外散性的特点,提出了一种基于近邻优化的跨域无监督行人重识别方法,首先采用有监督方法得到源域预训练模型,然后在目标域进行无监督训练。为增强模型对高相似度行人的辨识能力,设计了邻域对抗损失函数,任意样本与其他样本构成样本对,使类别确定性最强的一组样本对与不确定性最强的一组样本对之间进行对抗。为使类内样本特征朝着同一方向收敛,设计了特征连续性损失函数,将特征距离曲线进行中心归一化处理,在维持特征曲线固有差异的同时,拉近样本k邻近特征距离。结果消融实验结果表明损失函数各部分的有效性,对比实验结果表明,提出方法性能较已有方法更具优势,在Market-1501(1501 identities dataset from market)和DukeMTMC-reID(multi-target multi-camera person re-identification dataset from Duke University)数据集上的Rank-1和平均精度均值(mean average precision,mAP)指标分别达到了92.8%、84.1%和83.9%、71.1%。结论提出方法设计了邻域对抗损失与邻域连续性损失函数,增强了模型对相似人群的辨识能力,从而有效提升了行人重识别的性能。
Objective The purpose of pedestrian re-identification is to determine whether the people appearing in different camera scenes belong to the same person.This process can be regarded as a sub-problem of image retrieval and is widely used in intelligent video surveillance,criminal investigation,safety production,and other fields.Most of the pedestrian re-identification algorithms are designed with the supervised method based on known labels.These data are high expensive and are sometimes impossible to obtain.Most of the existing unsupervised pedestrian re-identification methods are based on loss functions,such as triplet loss,but have poor ability to distinguish similar identities.Compared with supervised pedes⁃trian recognition,unsupervised pedestrian recognition technology has greater application prospects.Although the image of pedestrians is partly affected by the shooting angle,light,camera parameters,pedestrian clothing,and other factors,pedestrian features also have strong regularity,such as intra-class feature convergence,inter-class feature divergence,and intra class feature consistency.Different scenes face different data distributions,and a large domain difference can be observed in real applications.The aforementioned problems lead to performance degeneration when transfer learning the model.Due to the great differences between the source and target domain data in image acquisition conditions and applica⁃tion scenarios,applying the source domain training model directly to the target domain will result in poor performance.Unsupervised domain adaptive(UDA)person re-identification aims to adapt the model trained on a labeled source domain to an unlabeled target domain.For pseudo-label-based UDA methods,pseudo label noise is the main problem for model degradation,while the cross-camera problem is one of the main factors that cause this noise.Method Aiming at the poor discriminative ability of similar pedestrians caused by pseudo-label noise,a cross-domain unsupervised pedestrian re-identification method b
作者
朱锦雷
李艳凤
陈后金
孙嘉
潘盼
Zhu Jinlei;Li Yanfeng;Chen Houjin;Sun Jia;Pan Pan(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)
出处
《中国图象图形学报》
CSCD
北大核心
2023年第11期3471-3484,共14页
Journal of Image and Graphics
基金
国家自然科学基金项目(62172029)
泉城产业领军人才创新团队项目(00982019010)。
