论文标题
朝着相机内的监督人员重新识别
Towards Precise Intra-camera Supervised Person Re-identification
论文作者
论文摘要
相机内的监督（ICS）对人重新识别（RE-ID）假设身份标签在每个相机视图中都独立注释，并且没有标记相机间的身份关联。这是一个新的环境，旨在减轻注释的负担，同时期望保持理想的重新ID绩效。但是，缺乏相机间标签使ICS重新ID问题比完全监督的同行更具挑战性。通过研究IC的特征，本文提出了摄像机特异性的非参数分类器，以及混合挖掘五胞胎损失，以执行相机内的学习。然后，进行了相机间的学习模块，该模块由基于图的ID关联步骤和重新ID模型更新步骤组成。在三个大规模重新ID数据集上进行的广泛实验表明，我们的方法的表现优于所有现有的ICS，其功能很大。我们的方法甚至可以与两个数据集中的最新监督方法相提并论。
Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view and no inter-camera identity association is labeled. It is a new setting proposed recently to reduce the burden of annotation while expect to maintain desirable Re-ID performance. However, the lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. By investigating the characteristics of ICS, this paper proposes camera-specific non-parametric classifiers, together with a hybrid mining quintuplet loss, to perform intra-camera learning. Then, an inter-camera learning module consisting of a graph-based ID association step and a Re-ID model updating step is conducted. Extensive experiments on three large-scale Re-ID datasets show that our approach outperforms all existing ICS works by a great margin. Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
