基于深度学习的多模态行人重识别综述

Multi-modal person re-identification based on deep learning:a review

行人重识别(Re-ID)旨在跨像机检索同一目标行人,它是智能视频监控领域的一项关键技术.由于监控场景的复杂性,单模态行人重识别在低光、雾天等极端情况下的适用性较差.因实际应用的需要以及深度学习的快速发展,基于深度学习的多模态行人重识别受到了广泛的关注.本文针对近年来多模态行人重识别的发展脉络进行综述:阐述了传统单模态行人重识别方法存在的不足;归纳了多模态行人重识别的常见应用场景及其优势,以及各数据集的构成;重点分析了各种场景下多模态行人重识别的相关方法及其分类,并探讨了当前研究的热点和挑战;最后,讨论了多模态行人重识别的未来发展趋势及其潜在应用价值.

Person re-identification(Re-ID),which involves retrieving the same person across cameras,is a key technology in the field of intelligent video surveillance.However,due to the complexity of surveillance scenarios,traditional single-modal approaches encounter limitations in extreme conditions such as low lighting and foggy days.Given the practical demands and the swift advancement in deep learning,multi-modal person Re-ID based on deep learning has received widespread attention.This article provides a review of the progress in multi-modal person Re-ID based on deep learning in recent years,elaborates on the shortcomings of traditional single-modal approaches and summarizes the common application scenarios and advantages of multi-modal person Re-ID,as well as the composition of various datasets.The article also highlights the relevant methods and classification of multi-modal person Re-ID across diverse scenarios,exploring current research hotspots and challenges.Finally,it discusses the future development trends and potential applications of multi-modal person Re-ID.