基于深度学习的行人再识别技术研究综述

Survey of person re-identification technology based on deep learning

行人再识别(Re-id)作为智能视频监控技术之一,其目的是在不同的摄像机视图中检索出指定身份的行人,因此该项技术对维护社会治安稳定具有重大研究意义。针对传统的手工特征方法难以应对行人Re-id任务中复杂的摄像机环境的问题,大量基于深度学习的行人Re-id方法被提出,极大地推动了行人Re-id技术的发展。为了深入了解基于深度学习的行人Re-id技术,整理和分析了大量相关文献,首先从图像、视频、跨模态这3个方面展开综述性介绍,将图像行人Re-id技术分为有监督和无监督两大类并分别进行概括;然后列举了部分相关数据集,并对近年来在图像和视频数据集上的一些算法进行性能的比较与分析;最后总结了行人Re-id技术的发展难点,并深入讨论了该技术未来可能的研究方向。

As one of intelligent video surveillance technologies,person Re-identification(Re-id)has great research significance for maintaining social order and stability,and it aims to retrieve the specific person in different camera views.For traditional hand-crafted feature methods are difficult to address the complex camera environment problem in person Re-id task,a large number of deep learning-based person Re-id methods were proposed,so as to promote the development of person Re-id technology greatly.In order to deeply understand the person Re-id technology based on deep learning,a large number of related literature were collated and analyzed.First,a comprehensive introduction was given from three aspects:image,video and cross-modality.The image-based person Re-id technology was divided into two categories:supervised and unsupervised,and the two categories were generalized respectively.Then,some related datasets were listed,and the performance of some algorithms in recent years on image and video datasets were compared and analyzed.At last,the development difficulties of person Re-id technology were summarized,and the possible future research directions of this technology were discussed.