The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that pedestrian targets need to be accurately re-identified across a network of multiple cameras with non-overlapping fields of views before other post-hoc high-level utilizations (i.e., tracking, behaviors analyses, activities monitoring, etc.) can be carried out. Driven by recent developments in deep learning techniques, the important re-id problem is often tackled via either deep discriminant learning or deep generative learning techniques. However, most contemporary deep learning-based models with tremendously deep structures are not easy to be trained because of the notorious vanishings gradient problem. In this study, a novel full-scaled deep discriminant learning model is proposed. The novelty of the full-scale model is significant, as three crucial concepts in designing a deep learning model, including depth, width, and cardinality, are all taken into consideration, simultaneously. Therefore, the new model needs not to be tremendously deep but is more convenient to be trained. Moreover, based on the new model, a novel deep metric learning method is proposed to further solve the important re-id problem. Technically, two algorithms either based on the conventional SGD (stochastic gradient descent) or an alternative more efficient PGD (proximal gradient descent) are both derived. For experimental analyses, the newly introduced full-scaled deep metric learning method has been comprehensively compared with dozens of popular re-id methods proposed from either deep learning or shallow learning perspectives. Several well-known public re-id datasets have been incorporated and rigorous statistical analyses have been carried out to compare all methods regarding their re-id performance. The superiority of the novel full-scaled deep metric learning method has been substantiated, from the statistical point of view.

行人重新识别问题（即re-id）是多摄像机视频监控研究中必不可少的前提，前提是必须跨多个不重叠摄像机的网络准确地重新确定行人目标可以执行其他事后高级利用（例如，跟踪，行为分析，活动监视等）之前的视图领域。在深度学习技术的最新发展推动下，重要的re-id问题通常通过深度判别学习或深度生成学习技术来解决。但是，由于臭名昭著的消失梯度问题，大多数当代的具有极大深度结构的基于深度学习的模型都不容易被训练。在这项研究中，提出了一种新颖的全面的深度判别学习模型。全面模型的新颖性意义重大，因为在设计深度学习模型时必须同时考虑三个关键概念，包括深度，宽度和基数。因此，新模型不需要太深，但是更易于训练。此外，在新模型的基础上，提出了一种新的深度度量学习方法，以进一步解决重要的re-id问题。从技术上讲，两种算法都是基于常规SGD（随机梯度下降）或一种更有效的PGD（近端梯度下降）得出的。对于实验分析，已将新引入的全面深度度量学习方法与从深度学习或浅层学习角度提出的数十种流行的re-id方法进行了全面比较。已合并了几个著名的公共re-id数据集，并进行了严格的统计分析以比较所有关于其re-id性能的方法。从统计的角度来看，这种新颖的全面深度度量学习方法的优越性得到了证实。