Research on dense crowd counting method combining attention and hybrid dilated convolution

Hongxia Wang; Tao Wu

doi:10.1117/12.2675190

25 May 2023 Research on dense crowd counting method combining attention and hybrid dilated convolution

Hongxia Wang, Tao Wu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126365F (2023) https://doi.org/10.1117/12.2675190
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

To solve the problems of background interference, noise and occlusion in crowd counting in complex scenes, a CA-HDC crowd counting method was proposed. The network consists of the front end and the back end. The front end selects the first 10 layers of the VGG-16 network. In the back end, the SE attention mechanism is fused with the hybrid cavity convolution layer that alternately uses the expansion rate. The head features are weighted by the attention mechanism, and the receptive field of the network is expanded by the hybrid cavity convolution to eliminate the influence of background interference and noise. Experiments are carried out on two public data sets. Compared with other crowd counting networks, the proposed network achieves better results.

Citation Download Citation

Hongxia Wang and Tao Wu "Research on dense crowd counting method combining attention and hybrid dilated convolution", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126365F (25 May 2023); https://doi.org/10.1117/12.2675190

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Image processing

Head

Convolutional neural networks

Feature extraction

Background noise

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years