A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting

A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting

Blog Article

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 Sequin Pillow 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 Dessert Spoons 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.