Improving Person Re-identification via Pose-Aware Multi-shot Matching

Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse...

Full description

Saved in:
Bibliographic Details
Published in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1354 - 1362
Main Authors Yeong-Jun Cho, Kuk-Jin Yoon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Cameras
Feature extraction
Learning systems
Measurement
Reliability
Smoothing methods
Videos
Online AccessGet full text

Cover

More Information
Summary:Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person reidentification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and efficiently conducts multi-shot matching based on the target pose information. Experimental results using public person reidentification datasets show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.151