Peihong Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Peihong Ma has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Meng Jia
The School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
1 shared publication - Xiaofan Wang
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
1 shared publication - Xinhong Hei
The School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
1 shared publication - Zhiqiang Zhao
The School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
1 shared publication
Latest Publications
- Open Access
A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting
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.
Open AccessA Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting
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.