The huge variance of human pose and the misalignment of detected human images
significantly increase the difficulty of person Re-Identification (Re-ID).
Moreover, efficient Re-ID systems are required to cope with the massive visual
data being produced by video surveillance systems. Targeting to solve these
problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an
efficient indexing and retrieval framework, respectively. GLAD explicitly
leverages the local and global cues in human body to generate a discriminative
and robust representation. It consists of part extraction and descriptor
learning modules, where several part regions are first detected and then deep
neural networks are designed for representation learning on both the local and
global regions. A hierarchical indexing and retrieval framework is designed to
eliminate the huge redundancy in the gallery set, and accelerate the online
Re-ID procedure. Extensive experimental results show GLAD achieves competitive
accuracy compared to the state-of-the-art methods. Our retrieval framework
significantly accelerates the online Re-ID procedure without loss of accuracy.
Therefore, this work has potential to work better on person Re-ID tasks in real
scenarios.

本文提出了一个新的人物重识别的框架，该框架利用全局 - 本地对齐描述符并采用高效的索引与检索机制，通过提取人物局部区域和使用深度神经网络进行局部和全局区域的表示学习，实现了与现有最先进技术相媲美的准确度和在线重识别加速的效果。