Survey on Cross-modality Object Re-identification Research
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 visi-ble 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 pro-gress.However,a comprehensive summary and in-depth analysis of existing models are still lacking.To this end,this paper con-ducts an in-depth investigation and summary of relevant research and novel methods in the field of Ⅵ-ReID.It discusses the chal-lenges faced by existing methods in actual scenarios,and categorizes them from two aspects:model classification and model evalu-ation.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.
NETL
NSTL
万方数据
