Multi-view Based Pose Alignment Method for Person Re-identification

摘要：

　　This paper proposes a Multi-View based Pose Alignment(MVPA)method for person re-identification(re-id).Most recent methods solve re-id as a matching process based on single image.However,when poses vary or viewpoints change,the performance seriously deteriorates.This paper aims to learn a representation insensitive to view and pose.Specifically,we establish a set of Multi-view based Person Pose Templates(MPPT)and propose a Pose-Guided Person image Generation(iPG2)model to synthesize multi-view and uniform-pose based images.The representation learned from multi-view images can significantly enhances the accuracy of re-id.We evaluate our method on two popular datasets,i.e.,Market-1501 and DukeMTMC-reID.The results show that our framework promotes the performance of re-id a lot and surpass other methods.

关键词： Person re-identification Pose Alignment Generative Adversarial Networks

作者: Yulei Zhang Qingjie Zhao You Li

作者单位: Beijing Key Lab of Intelligent Information Technology,School of Computer Science,Beijing Institute of Technology,Beijing 100081,China

会议类型: 国际会议

会议名称: 2019中国智能自动化大会(CIA,2019)

会议地点: 江苏镇江

会议语种:英文

页码: 439-447

在线出版日期: 2019-09-20（万方平台首次上网日期，不代表论文的发表时间）