An Empirical Study of Person Re-Identification with Attributes
An Empirical Study of Person Re-Identification with Attributes
复制标题
DOI：
10.1109/ro-man46459.2019.8956459
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Vikram Shree;Wei-Lun Chao;M. Campbell
中科院分区：
文献类型：
--
作者：
Vikram Shree;Wei-Lun Chao;M. Campbell
人物重新识别的目的是从图像集合中识别出一个人，给定该人的一个图像作为查询。然而，在现实生活中，我们可能没有查询图像的先验库，因此必须依赖于来自其他模态的信息。在本文中，提出了一种基于属性的方法，其中感兴趣的人（POI）描述的一组视觉属性，这是用来执行搜索。我们比较了多种算法，并分析了属性的质量如何影响性能。虽然以前的工作主要依赖于专家注释的高精度属性，但我们进行了一项人体研究，发现某些视觉属性无法被人类观察者一致地描述，这使得它们在真实的应用中不太可靠。一个关键的结论是，非专家属性，而不是专家注释的，实现的性能是一个更忠实的指标的现状，基于属性的方法，人的重新识别。
Person re-identification aims to identify a person from an image collection, given one image of that person as the query. There is, however, a plethora of real-life scenarios where we may not have a priori library of query images and therefore must rely on information from other modalities. In this paper, an attribute-based approach is proposed where the person of interest (POI) is described by a set of visual attributes, which are used to perform the search. We compare multiple algorithms and analyze how the quality of attributes impacts the performance. While prior work mostly relies on high precision attributes annotated by experts, we conduct a human-subject study and reveal that certain visual attributes could not be consistently described by human observers, making them less reliable in real applications. A key conclusion is that the performance achieved by non-expert attributes, instead of expert-annotated ones, is a more faithful indicator of the status quo of attribute-based approaches for person re-identification.