Survey on person re‐identification based on deep learning. Issue 4 (11th July 2018)

• Record Type: Journal Article
• Authors: Wang, Kejun; Wang, Haolin; Liu, Meichen; Xing, Xianglei; Han, Tian
• Is Part Of: CAAI transactions on intelligence technology. Volume 3:Issue 4(2018)
• Page Start: 219
• Page End: 227
• Publication Date: 2018-07-11
• Subjects: computer vision -- learning (artificial intelligence) -- image matching -- image representation -- feature extraction; deep learning -- computer vision technologies -- person illumination -- occlusion -- attitude change -- person Re‐ID research -- person Re‐ID methods -- person reidentification -- person Re‐ID frameworks -- person matching -- feature‐based method -- metric‐based method -- person representation; B6135E Image recognition -- C5260B Computer vision and image processing techniques -- C6170K Knowledge engineering techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1001
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Person re‐identification (Re‐ID) is a fundamental subject in the field of the computer vision technologies. The traditional methods of person Re‐ID have difficulty in solving the problems of person illumination, occlusion and attitude change under complex background. Meanwhile, the introduction of deep learning opens a new way of person Re‐ID research and becomes a hot spot in this field. This study reviews the traditional methods of person Re‐ID, then the authors focus on the related papers about different person Re‐ID frameworks on the basis of deep learning, and discusses their advantages and disadvantages. Finally, they propose the direction of further research, especially the prospect of person Re‐ID methods based on deep learning.