Person Re Identification
Person re-identification (ReID) focuses on matching images of the same individual across different camera views, a crucial task in surveillance and security. Current research emphasizes improving ReID's robustness to variations in appearance (e.g., clothing changes, occlusions, lighting), viewpoint, and even across different camera modalities (e.g., aerial and ground views), often employing transformer networks, graph convolutional networks, and generative adversarial networks to learn more discriminative and generalizable features. These advancements are driving progress in applications like video surveillance, robotics, and even privacy-preserving ReID systems, impacting both the accuracy and efficiency of person identification technologies.
Papers
OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization
Saihui Hou, Panjian Huang, Zengbin Wang, Yuan Liu, Zeyu Li, Man Zhang, Yongzhen Huang
Domain Consistency Representation Learning for Lifelong Person Re-Identification
Shiben Liu, Qiang Wang, Huijie Fan, Weihong Ren, Baojie Fan, Yandong Tang
Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matching
Hye-Geun Kim, Yong-Hyuk Moon, Yeong-Jun Cho
PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement
Delong Zhang, Yi-Xing Peng, Xiao-Ming Wu, Ancong Wu, Wei-Shi Zheng
PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification
Bin Hu, Xinggang Wang, Wenyu Liu