Home // CENTRIC 2017, The Tenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services // View article

Person Re-identification in Crowded Scenes with Deep Learning

Authors:
Yupeng Wang
Huiyuan Fu
Shuangqun Li

Keywords: deep learning; person re-identification; crowded scenes; convolutional neural network; loss function

Abstract:
Person re-identification in crowded scenes is very important. Most images come from different surveillance video and cameras, and one person may look different in a variety of scenes, viewpoints, lighting and so on. The existing methods have limited effects in practical applications. In this paper, we propose a convolutional neural network for person re-identification in crowded scenes. The model structure of this network combines pedestrian detection and re-identification. In addition, we propose a loss function to better match the target person by calculating Pearson correlation evaluation. The experimental results illustrate the superior performance of our method.

Pages: 14 to 17

Copyright: Copyright (c) IARIA, 2017

Publication date: October 8, 2017

Published in: conference

ISSN: 2308-3492

ISBN: 978-1-61208-592-0

Location: Athens, Greece

Dates: from October 8, 2017 to October 12, 2017