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 …

Detection and learning based appearance feature play the central role in data association
based multiple object tracking (MOT), but most recent MOT works usually ignore them and …

Most existing person re-identification (re-id) methods focus on learning the optimal distance
metrics across camera views. Typically a person's appearance is represented using features …

Person re-identification has been usually solved as either the matching of single-image
representation (SIR) or the classification of cross-image representation (CIR). In this work …

The visual appearance of a person is easily affected by many factors like pose variations,
viewpoint changes and camera parameter differences. This makes person Re-Identification …

Pose variation remains one of the major factors that adversely affect the accuracy of person
re-identification. Such variation is not arbitrary as body parts (eg head, torso, legs) have …

We address the person re-identification problem by effectively exploiting a globally
discriminative feature representation from a sequence of tracked human regions/patches …

Most existing person re-identification (re-id) models focus on matching still person images
across disjoint camera views using the setting of either single-shot or multi-shot. Since …

In this paper, we propose a deep end-to-end neu-ral network to simultaneously learn high-
level features and a corresponding similarity metric for person re-identification. The network …

Current person re-identification (ReID) methods typically rely on single-frame imagery
features, whilst ignoring space-time information from image sequences often available in the …