Shallow Feature Based Dense Attention Network for Crowd Counting

06/17/2020
by   Yunqi Miao, et al.
0

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

READ FULL TEXT

page 1

page 2

page 3

page 5

research
04/06/2021

Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting

Crowd counting aims to predict the number of people and generate the den...
research
01/05/2021

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

Crowd counting is an important task that shown great application value i...
research
08/02/2021

Shallow Attention Network for Polyp Segmentation

Accurate polyp segmentation is of great importance for colorectal cancer...
research
08/09/2020

SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting

Automated crowd counting from images/videos has attracted more attention...
research
07/04/2021

SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images

With the increasing demand for search and rescue, it is highly demanded ...
research
12/17/2021

Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network

The paper focuses on improving the recent plug-and-play patch rescaling ...
research
03/16/2023

GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision

Airway segmentation, especially bronchioles segmentation, is an importan...

Please sign up or login with your details

Forgot password? Click here to reset