This paper presents an end-to-end learning architecture for video-based person re-
identification by integrating convolutional neural networks (CNNs) and bidirectional …

Person re-identification aims at matching the identity of a same person that is captured from
non-overlapping cameras. Most of the existing person re-identification methods are still …

The goal of video-based person re-identification is to match different pedestrians in various
image sequences across non-overlapping cameras. A critical issue of this task is how to …

Surveillance cameras have been widely used in different scenes. Accordingly, a demanding
need is to recognize a person under different cameras, which is called person re …

In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal
features and corresponding similarity metric for video-based person re-identification. Given …

Person re-identification is an important task in video surveillance, focusing on finding the
same person across different cameras. However, most existing methods of video-based …

This paper proposes a two-stream convolution network to extract spatial and temporal cues
for video based person ReIdentification (ReID). A temporal stream in this network is …

This paper presents a novel approach for video-based person re-identification using
multiple convolutional neural networks (CNNs). Unlike the previous work, we intend to …

In this paper, we propose a novel deep end-to-end network to automatically learn the spatial-
temporal fusion features for video-based person re-identification. Specifically, the proposed …

Video-based person re-identification (ReID) is a challenging problem, where some video
tracks of people across non-overlapping cameras are available for matching. Feature …