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.