The automatization of the Re-Identification of an individual across different video-surveillance cameras poses a significant challenge due to the presence of a vast number of potential candidates with a similar appearance. This task requires the learning of discriminative features from person images and a distance metric to properly compare them and decide whether they belong to the same person or not. Nevertheless, the fact of acquiring images of the same person from different, distant and non-overlapping views produces changes in illumination, perspective, background, resolution and scale between the person’s representations, resulting in appearance variations that hamper his/her re-identification. This article focuses the feature learning on automatically finding discriminative descriptors able to reflect the dissimilarities mainly due to the changes in actual people appearance, independently from the variations introduced by the acquisition point. With that purpose, such variations have been implicitly embedded by the Mahalanobis distance. This article presents a learning algorithm to jointly model features and the Mahalanobis distance through a Deep Neural Re-Identification model. The Mahalanobis distance learning has been implemented as a novel neural layer, forming part of a Triplet Learning model that has been evaluated over PRID2011 dataset, providing satisfactory results.

中文翻译：

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通过深度三重态学习模型对人的马哈拉诺比斯距离进行反向传播以重新识别人

由于存在大量具有相似外观的潜在候选者，跨不同视频监控摄像机的个人重新识别的自动化带来了巨大的挑战。此任务需要从人像中学习区分特征和距离度量，以正确比较它们并确定它们是否属于同一个人。然而，从不同的，遥远的和不重叠的视图获取同一个人的图像的事实在该个人的表示之间改变了照明，视角，背景，分辨率和比例，从而导致外观变化，从而妨碍了他/她的重新识别。本文将特征学习的重点放在自动查找能够反映差异的描述符，这些描述符主要反映实际人物外观的变化，而与获取点引入的变化无关。为此，马氏距离隐式地嵌入了这种变化。本文提出了一种通过深度神经网络重新识别模型对特征和马氏距离进行联合建模的学习算法。Mahalanobis远程学习已被实现为一种新型的神经层，构成了Triplet学习模型的一部分，该模型已通过PRID2011数据集进行了评估，从而提供了令人满意的结果。马氏距离隐含地嵌入了这些变化。本文提出了一种通过深度神经网络重新识别模型对特征和马氏距离进行联合建模的学习算法。Mahalanobis远程学习已被实现为一种新型的神经层，构成了Triplet学习模型的一部分，该模型已通过PRID2011数据集进行了评估，从而提供了令人满意的结果。马氏距离隐含地嵌入了这些变化。本文提出了一种通过深度神经网络重新识别模型对特征和马氏距离进行联合建模的学习算法。Mahalanobis远程学习已被实现为一种新型的神经层，构成了Triplet学习模型的一部分，该模型已通过PRID2011数据集进行了评估，从而提供了令人满意的结果。