Shallow Feature Based Dense Attention Network for Crowd Counting release_pydjh63oozdvlinleqfzrpyj4m

by Yunqi Miao, Zijia Lin, Guiguang Ding, Jungong Han

Published in PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE by Association for the Advancement of Artificial Intelligence (AAAI).

2020   Volume 34, Issue 07, p11765-11772

Abstract

While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and human crowds generally have noticeably different responses in shallow features, we decide to build our attention model upon shallow-feature maps, which results in accurate background-pixel detection. Moreover, considering that the most representative features of people across different scales can appear in different layers of a feature extraction network, to better keep them all, we propose to densely connect hierarchical image features of different layers and subsequently encode them for estimating crowd density. Experimental results on three benchmark datasets clearly demonstrate the superiority of SDANet when dealing with different scenarios. Particularly, on the challenging UCF_CC_50 dataset, our method outperforms other existing methods by a large margin, as is evident from a remarkable 11.9% Mean Absolute Error (MAE) drop of our SDANet.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.5 MB
file_6pslx7feuvgpnhnl3lj6whorge
aaai.org (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2020-04-03
Proceedings Metadata
Not in DOAJ
Not in Keepers Registry
ISSN-L:  2159-5399
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: f98b68d0-91da-4dec-a8d6-09a3ac66324f
API URL: JSON