Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.

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Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽

10.3390/sym13040703 ◽

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.

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A High-Density Crowd Counting Method Based on Convolutional Feature Fusion

Applied Sciences ◽

10.3390/app8122367 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2367 ◽

Cited By ~ 5

Author(s):

Hongling Luo ◽

Jun Sang ◽

Weiqun Wu ◽

Hong Xiang ◽

Zhili Xiang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Feature Fusion ◽

High Density ◽

Counting Problem ◽

Crowd Counting ◽

Low Level ◽

Density Map ◽

High Level

In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environment, while there exists technology drawbacks and a lack of mature systems. Aiming to solve the crowd counting problem with high-density under complex environments, a feature fusion-based deep convolutional neural network method FF-CNN (Feature Fusion of Convolutional Neural Network) was proposed in this paper. The proposed FF-CNN mapped the crowd image to its crowd density map, and then obtained the head count by integration. The geometry adaptive kernels were adopted to generate high-quality density maps which were used as ground truths for network training. The deconvolution technique was used to achieve the fusion of high-level and low-level features to get richer features, and two loss functions, i.e., density map loss and absolute count loss, were used for joint optimization. In order to increase the sample diversity, the original images were cropped with a random cropping method for each iteration. The experimental results of FF-CNN on the ShanghaiTech public dataset showed that the fusion of low-level and high-level features can extract richer features to improve the precision of density map estimation, and further improve the accuracy of crowd counting.

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