Abstract: Object re-identification(ReID) technology aims to match the same object captured by cameras across different areas at different time.The key is to distinguish different objects through fine-grained differences between different individuals,which is widely used in security control,criminal investigation and monitoring,etc.Traditional ReID technology is usually suitable for visible cameras with good lighting conditions,but its performance is severely limited under low-light conditions.The infrared camera is often used to collect infrared images of objects under low light conditions due to its outstanding night vision performance.Therefore,cross-modality object re-identification technology focuses on achieving uninterrupted object ReID across day and night from visible images to infrared images(VI-ReID),and vice versa.In recent years,VI-ReID technology has made significant progress.However,a comprehensive summary and in-depth analysis of existing models are still lacking.To this end,this paper conducts an in-depth investigation and summary of relevant research and novel methods in the field of VI-ReID.It discusses the challenges faced by existing methods in actual scenarios,and categorizes them from two aspects:model classification and model evaluation.First,focusing on the research challenges,VI-ReID is categorized into generative methods and non-generative methods.Se-condly,the evaluation datasets and evaluation metrics are reviewed and summarized.Finally,the remaining challenges in VI-ReID are discussed and the future development trends are prospected.

Key words: Computer vision, Object re-identification, Cross-modality, Fine-grained feature, Representation learning