Person Re-identification by Efficient Impostor-Based Metric Learning

Martin Hirzer, P. Roth, H. Bischof
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引用次数: 170

Abstract

Recognizing persons over a system of disjunct cameras is a hard task for human operators and even harder for automated systems. In particular, realistic setups show difficulties such as different camera angles or different camera properties. Additionally, also the appearance of exactly the same person can change dramatically due to different views (e.g., frontal/back) of carried objects. In this paper, we mainly address the first problem by learning the transition from one camera to the other. This is realized by learning a Mahalanobis metric using pairs of labeled samples from different cameras. Building on the ideas of Large Margin Nearest Neighbor classification, we obtain a more efficient solution which additionally provides much better generalization properties. To demonstrate these benefits, we run experiments on three different publicly available datasets, showing state-of-the-art or even better results, however, on much lower computational efforts. This is in particular interesting since we use quite simple color and texture features, whereas other approaches build on rather complex image descriptions!
基于高效冒充人度量学习的人物再识别
在一个由互不相连的摄像头组成的系统中识别人对人类操作员来说是一项艰巨的任务,对自动化系统来说就更难了。特别是,现实设置显示困难,如不同的相机角度或不同的相机属性。此外,同一个人的外观也会因为不同的视角(例如,正面/背面)而发生巨大变化。在本文中,我们主要通过学习从一个摄像机到另一个摄像机的过渡来解决第一个问题。这是通过使用来自不同相机的成对标记样本来学习马氏度规来实现的。基于大边界最近邻分类的思想,我们得到了一个更有效的解决方案,并且提供了更好的泛化特性。为了证明这些好处,我们在三个不同的公开可用数据集上运行实验,显示了最先进甚至更好的结果,然而,在更低的计算工作量上。这是特别有趣的,因为我们使用相当简单的颜色和纹理特征,而其他方法建立在相当复杂的图像描述!
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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