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

Tang, Siqi; Pan, Zhisong; Zhou, Xingyu

doi:10.1155/2017/5046727

Cited by 5 publications

(5 citation statements)

References 37 publications

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“…Also, it can be concluded from Table 2 that the results of FF-CNN are much better than Cross-scene [4], MCNN [16], FCN [29], Cascaded-MTL [30] and Switching-CNN [17] method. Compared to LFCNN [20] and SaCNN [21], the proposed FF-CNN method shows lower error on the crowd-intensive Part A dataset. MAE and MSE for LFCNN are reduced by 7.45 and 3.1, respectively.…”

Section: Results and Analysismentioning

confidence: 94%

“…Tang et al [20] proposed a low-rank and sparse-based deep-fusion convolutional neural network for crowd counting (LFCNN) by adopting a regression method based on low-rank and sparse penalty to promote the accuracy of the projection from the density map to global counting, which got an excellent performance. Zhang et al [21] proposed scale-adaptive CNN (SaCNN) to estimate the crowd density map and integrate the density map to get a more accurate estimated head count by extracting feature maps from multiple layers and adapted them to have the same output size. Han et al [22] combined convolutional neural network and Markov Random Field (CNN-MRF) to achieve the head count in static images which contained three parts: a pre-trained deep residual network 152 [23] to extract features, a fully connected neural network for count regress and a MRF to smooth the counting results of the local patches.…”

Section: Introductionmentioning

confidence: 99%

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

Luo

Sang

et al. 2018

Applied Sciences

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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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Section: Results and Analysismentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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

Luo

Sang

et al. 2018

Applied Sciences

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“…The network takes arbitrary size image and outputs a density map. Tang et al[58] propose fusion CNN that has two key stages. The first stage adopts deep-fusion network to estimate the crowd density and the second stage employs regression to estimate the count.…”

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confidence: 99%

A Deep-Fusion Network for Crowd Counting in High-Density Crowded Scenes

Khan

Salih²,

Zafar³

et al. 2021

Int J Comput Intell Syst

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People counting has been investigated extensively as a tool to increase the individual’s safety and to avoid crowd hazards at public places. It is a challenging task especially in high-density environment such as Hajj and Umrah, where millions of people gathered in a constrained environment to perform rituals. This is due to large variations of scales of people across different scenes. To solve scale problem, a simple and effective solution is to use an image pyramid. However, heavy computational cost is required to process multiple levels of the pyramid. To overcome this issue, we propose deep-fusion model that efficiently and effectively leverages the hierarchical features exits in various convolutional layers deep neural network. Specifically, we propose a network that combine multiscale features from shallow to deep layers of the network and map the input image to a density map. The summation of peaks in the density map provides the final crowd count. To assess the effectiveness of the proposed deep network, we perform experiments on three different benchmark datasets, namely, UCF_CC_50, ShanghaiTech, and UCF-QNRF. From experiments results, we show that the proposed framework outperforms other state-of-the-art methods by achieving low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.

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“…Crowd research is an important research content in video surveillance and intelligent images [1][2][3]. Among them, crowd counting [4][5][6] is a key research point, whose purpose is to automatically calculate the number of crowds in images or videos or predict the density maps of di erent dense scenes. With the continuous growth of the world population and the increase of human diversi ed social activities, the situation of large population gatherings often appears in our daily life.…”

Section: Introductionmentioning

confidence: 99%

SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

Xiong

Huang

et al. 2022

Mathematical Problems in Engineering

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Accurate counting in dense scenes can effectively prevent the occurrence of abnormal events, which is crucial for flow management, traffic control, and urban safety. In recent years, the application of deep learning technology in counting tasks has significantly improved the performance of models, but it still faces many challenges, including the diversity of target distribution between image and background, the drastic change of target scale, and serious occlusion. To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality estimated density maps. SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then calibrates and refuses the fused feature maps through a channel spatial attention-aware module, which improves the model’s ability to suppress background and focus on main features. Finally, the final estimated density map is generated by a dilated convolution module. We conduct experiments on five public crowd datasets, UCF_CC_50, WorldExpo’10, ShanghaiTech, Mall, and Beijing BRT, and the results show that our method achieves lower counting errors than existing state-of-the-art methods. In addition, we extend SCFFNet to count other objects, such as vehicles in the vehicle dataset HBR_YD, and the experimental results show that our proposed method significantly improves the output quality with higher accuracy than previous methods.

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

Cited by 5 publications

References 37 publications

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

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

A Deep-Fusion Network for Crowd Counting in High-Density Crowded Scenes

SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

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