Dense crowd counting from still images with convolutional neural networks

Hu, Yi; Chang, Huan; Nian, Fudong; Wang, Yan; Li, Teng

doi:10.1016/j.jvcir.2016.03.021

Cited by 109 publications

(64 citation statements)

References 31 publications

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“…Similar to other algorithms [7][8][9][10][11][12][13], in this study, the 601st to 1400th frames are used as training samples, and the remaining 1200 frames are test samples. Likewise, the foreground of the 2000 frames is extracted and blocked, and the people count of each block is obtained through the annotations.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…For the comparison of people count estimation, mean absolute error (MAE) and mean relative error (MRE) are used as the criteria [7][8][9][10][11][12][13].…”

Section: Experiments Results and Analysismentioning

confidence: 99%

“…To further verify the effectiveness of the proposed method, algorithms in [7][8][9][10][11][12][13] and the proposed method are tested on UCSD public data set. 1.70 9.3 Hu et al [12] 1.98 10.8 Zhang et al [13] 1.60 8.7 The comparison of the accuracy of people count prediction is shown in Table 5.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

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Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

Zhang

Zheng

2017

Journal of Electrical and Computer Engineering

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Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it adopts the first-level SVR model to roughly predict the three features extracted from image blocks, including D-SIFT, ULBP, and GIST, and the prediction results are used as new features for the second-level SVR model for fine prediction. The prediction results of all image blocks are added for density estimation according to the crowd levels predefined for different scenes of scenic spots. Experimental results demonstrate that the proposed method can achieve a classification rate over 85% for multiple scenes of scenic spots, and it is an effective CDE method with strong adaptability.

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Section: Experiments Results and Analysismentioning

confidence: 99%

“…For the comparison of people count estimation, mean absolute error (MAE) and mean relative error (MRE) are used as the criteria [7][8][9][10][11][12][13].…”

Section: Experiments Results and Analysismentioning

confidence: 99%

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Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

Zhang

Zheng

2017

Journal of Electrical and Computer Engineering

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“…The CNN based counting regression methods, such as Patch-count CNN [25], Patch-multitask CNN [26], and TSCCM [27], applied fully connected layers to directly regress the counting of person in image patches. Though these methods constructed end-to-end network for counting task, a surveillance frame needs to be cut into amounts of patches with each patch counted by the network, which is fairly timeconsuming.…”

Section: Crowd Countingmentioning

confidence: 99%

“…(i) Location-based method including ACF (Aggregate Channel Feature) [3] (ii) Feature-based regression methods, including LBP [40] combined with Ridge Regression, LBP with LSSVM [41] regression, and Gabor [42] with LSSVM regression (iii) CNN based density map regression methods, including Patch-count CNN [25], Patch-multitask CNN [26], TSCCM [27], MCNN [12], Long-short CNN [13], and Hydra-CNN [14].…”

Section: Experiments Detailsmentioning

confidence: 99%

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

Tang

Pan

Zhou

2017

Mathematical Problems in Engineering

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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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Implementation of Real Time Reconfigurable Embedded Architecture for People Counting in a Crowd Area

Gong

Bourennane

2018

Modelling and Implementation of Complex Systems

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We propose a feature fusion method for crowd counting. By image feature extraction and texture feature analysis methods, data obtained from multiple sources are used to count the crowd. We count people in high density static images. Most of the existed people counting methods only work in small areas, such as office corridors, parks, subways and so on. Our method uses only static images to estimate the count in high density images (hundreds or even thousands of people), for example, large concerts, National Day parade. At this scale, we can't rely on only one set of features for counting estimation. Therefore, we use multiple sources of information, namely, HOG and LBP. These sources provide separate estimates and other combinations of statistical measurements. Using the support vector machine (SVM) classification technique, and regression analysis, we count the crowd with high density. The method gives good results in crowded scenes.

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Dense crowd counting from still images with convolutional neural networks

Cited by 109 publications

References 31 publications

Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

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

Implementation of Real Time Reconfigurable Embedded Architecture for People Counting in a Crowd Area

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