Multi-scale dilated convolution of convolutional neural network for crowd counting

2019 ◽  
Vol 79 (1-2) ◽  
pp. 1057-1073 ◽  
Author(s):  
Yanjie Wang ◽  
Shiyu Hu ◽  
Guodong Wang ◽  
Chenglizhao Chen ◽  
Zhenkuan Pan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crowd Counting ◽  
Multi Scale ◽  
Dilated Convolution

Multi-scale dilated convolution of convolutional neural network for image denoising

2019 ◽  
Vol 78 (14) ◽  
pp. 19945-19960 ◽  
Author(s):  
Yanjie Wang ◽  
Guodong Wang ◽  
Chenglizhao Chen ◽  
Zhenkuan Pan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Multi Scale ◽  
Dilated Convolution

Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks

2020 ◽  
Vol 30 (1) ◽  
pp. 180-191
Author(s):  
Liping Zhu ◽  
Hong Zhang ◽  
Sikandar Ali ◽  
Baoli Yang ◽  
Chengyang Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Density Estimation ◽  
Large Scale ◽  
Receptive Fields ◽  
Crowd Counting ◽  
Multi Scale ◽  
Training Scheme ◽  
Joint Training ◽  
End To End

Abstract The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches.

A novel multi-scale convolutional neural network for motor imagery classification

2021 ◽  
Vol 68 ◽  
pp. 102747
Author(s):  
Mouad Riyad ◽  
Mohammed Khalil ◽  
Abdellah Adib
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Multi Scale

scholarly journals Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Jun Zhang ◽  
Jiaze Liu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Estimation Error ◽  
Image Features ◽  
Urban Rail Transit ◽  
Crowd Counting ◽  
Passenger Flow ◽  
Urban Rail ◽  
Density Map ◽  
Flow Detection

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

scholarly journals Chicken Image Segmentation via Multi-Scale Attention-Based Deep Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wei Li ◽  
Yang Xiao ◽  
Xibin Song ◽  
Na Lv ◽  
Xinbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Multi Scale
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