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Person re-identification by integrating metric learning and support vector machine

机译：集成度量学习和支持向量机的人员重新识别

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摘要

Person re-identification (PRID) refers to a technology of matching person across non-overlapping camera views. Metric learning is one of the most commonly used methods in PRID. However, most of them do not explore the label information carried by the labeled training samples, which limits the improvement of recognition performance. To this end, we propose a joint learning method by integrating the discriminative metric learning, the support vector machine (SVM) and the identity discriminator into one model, so as to realize joint construction of metric learning and identity discriminator. In this process, the label information carried by the training samples is fully exploited and the latent identify space of pedestrians is constructed by predicting the person's identity. To mitigate the appearance ambiguity caused by the variations in camera views, body poses, illumination and occlusion, we develop an extreme distance regularization term and introduce it into the joint learning framework to refine the solution spaces of the metric learning and discriminator. Finally, we present a similarity measure method by combining the advantages of the metric learning and the identity discriminator. Experimental results on several benchmark datasets show that the proposed method significantly outperforms some state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：人员重新识别（PRID）是指在不重叠的摄像机视图中匹配人员的技术。度量学习是PRID中最常用的方法之一。但是，它们中的大多数都没有探索带标签的训练样本所携带的标签信息，这限制了识别性能的提高。为此，我们提出了一种将判别式度量学习，支持向量机（SVM）和身份鉴别器集成为一个模型的联合学习方法，以实现度量学习和身份鉴别器的联合构建。在这个过程中，训练样本所携带的标签信息被充分利用，并且通过预测人的身份来构造行人的潜在识别空间。为了减轻由摄像机视图，身体姿势，照明和遮挡的变化引起的外观模糊性，我们开发了一个极端距离正则化术语并将其引入联合学习框架，以完善度量学习和鉴别器的解决方案空间。最后，我们结合度量学习和身份识别器的优点，提出了一种相似性度量方法。在一些基准数据集上的实验结果表明，该方法明显优于某些最新方法。 （C）2019 Elsevier B.V.保留所有权利。

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