Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …

Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …

Feature extraction and matching are two crucial components in person Re-Identification
(ReID). The large pose deformations and the complex view variations exhibited by the …

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a
critical step to improve its accuracy. Yet in the re-ID community, limited effort has been …

Person re-identification (re-ID) has become increasingly popular in the community due to its
application and research significance. It aims at spotting a person of interest in other …

The superiority of deeply learned pedestrian representations has been reported in very
recent literature of person re-identification (re-ID). In this article, we consider the more …

This paper contributes a new high quality dataset for person re-identification, named" Market-
1501". Generally, current datasets: 1) are limited in scale; 2) consist of hand-drawn bboxes …

We consider the recovery of a low rank real-valued matrix M given a subset of noisy discrete
(or quantized) measurements. Such problems arise in several applications such as …

Learning discriminative representations for unseen person images is critical for person re-
identification (ReID). Most of the current approaches learn deep representations in …

Person re-identification is to match pedestrian images from disjoint camera views detected
by pedestrian detectors. Challenges are presented in the form of complex variations of …