Abstract:

With the continuous development of computer vision technology, pedestrian re-identification technology has played a huge role in the fields of security, detection and intelligent surveillance, and has become a current research hotspot. The traditional pedestrian re-recognition technology focuses on the research of the visual information of the RGB image collected by the camera, and has achieved good results under laboratory conditions, but under adverse conditions such as poor lighting, occlusion of objects, and blurred image quality, the recognition rate of the algorithm has experienced a cliff-like decline. Nowadays, visual information does not only focus on RGB images, but also introduces information such as infrared images, depth images, and sketch portraits to improve the recognition rate of the algorithm. At the same time, the application of text information and spatiotemporal information also improves the performance of pedestrian re-recognition algorithms. However, due to the natural differences between the various modes, how to connect multiple kinds of information has become the main problem of multi-source information pedestrian re-identification research. This article combs the research papers on pedestrian re-identification with multiple sources of information published in recent years, expounds the current situation, technical difficulties and future development trends of pedestrian re-identification.

Key words: pedestrian re-identification, deep learning, multi-source information pedestrian re-identification

摘要：

随着计算机视觉技术的不断发展，行人再识别技术在安防、侦查和智能监控等领域发挥了巨大的作用，成为了当下的研究热点。传统的行人再识别技术聚焦于摄像机采集到的可见光图像这一视觉信息的研究，并且在实验室条件下已经达到了较好的效果，但在光照情况差、目标遮挡、画质模糊等不利条件下，算法的识别率出现了断崖式的下降。如今视觉信息不单单再聚焦于可见光图像，而是引入了红外图像、深度图像、素描人像等信息用以提高算法的识别率。与此同时，文本信息和时空信息的应用同样也提升了行人再识别算法的性能。但由于各个模态间存在天然差异，如何连接多种信息成了多源信息行人再识别研究的主要问题。对近年公开发表的多源信息行人再识别研究论文的梳理，阐述了行人再识别的研究现状、技术困难以及未来的发展趋势。

关键词: 行人再识别, 深度学习, 多源信息行人再识别