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 …

In recent years, person re-identification (ReID) has received much attention since it is a
fundamental task in intelligent surveillance systems and has widespread application …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

This paper studies the unsupervised embedding learning problem, which requires an
effective similarity measurement between samples in low-dimensional embedding space …

The combination of global and partial features has been an essential solution to improve
discriminative performances in person re-identification (Re-ID) tasks. Previous part-based …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

Existing person re-identification (re-id) methods either assume the availability of well-
aligned person bounding box images as model input or rely on constrained attention …

Employing part-level features offers fine-grained information for pedestrian image
description. A prerequisite of part discovery is that each part should be well located. Instead …

Although the performance of person Re-Identification (ReID) has been significantly boosted,
many challenging issues in real scenarios have not been fully investigated, eg, the complex …

Recently, person re-identification (Re-ID) has achieved great progress. However, current
methods largely depend on color appearance, which is not reliable when a person changes …