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SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification

Authors :
Fan, Xing
Luo, Hao
Zhang, Xuan
He, Lingxiao
Zhang, Chi
Jiang, Wei
Publication Year :
2018

Abstract

Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.<br />Comment: accepted by ACCV 2018

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1810.06996
Document Type :
Working Paper