Ms. Yan Chen Profile

Yan Chen

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 May 2022 Paper

RA-MISNet: multilevel information sharing network based on residual attention for crowd counting

Jun Sang, Shaoli Tian, Jinghan Tan, Mohammad Alam, Yan Chen

Proceedings Volume 12101, 121010K (2022) https://doi.org/10.1117/12.2618271

KEYWORDS: Image segmentation, Feature extraction, Head, Data modeling, Network architectures, Convolution, Machine vision, Computer vision technology

Read Abstract +

Crowd counting has been a popular research topic in the field of computer vision due to the variation of human head scales and the interference of background noise. Some existing methods use multi-level feature fusion to solve scale variation, but the problem of background noise interference may be more serious due to the involvement of shallow features in the feature fusion process. In this paper, we propose a Multilevel Information Sharing Network based on Residual Attention(RA-MISNet) to solve this problem. The RA-MISNet consists of a feature extraction component, an information sharing module and a residual attention density map estimator. On the basis of solving the multi-scale problem, the residual attention mechanism is adopted by our proposed method to refine the population distribution information in sharing features at all levels, which can reduce the interference of complex texture background on density map regression. Furthermore, owing to the severe label noise interference problem in high-density crowd areas, we design a Regional Multi-level Segmentation Loss (RMS Loss) to divide the multi-level density regions with different label noise rates in a single crowd image and apply the corresponding granularity supervision constraints for each density level region. Extensive experiments on three crowd counting datasets (ShanghaiTech, UCF CC 50, UCF-QNRF) demonstrate the effectiveness and superiority of the proposed methods.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years