A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting.
Sensors (Basel)
; 24(6)2024 Mar 12.
Article
in En
| MEDLINE
| ID: mdl-38544079
ABSTRACT
Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Document type:
Article
Affiliation country:
China