Approaches on crowd counting and density estimation: a review

Li, Bo; Huang, Hongbo; Zhang, Ang; Liu, Peiwen; Cheng, Liu

doi:10.1007/s10044-021-00959-z

Cited by 50 publications

(18 citation statements)

References 95 publications

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“…Initially, the focus was on crowd counting using detection and regression [ 19 ]. Detection-based crowd counting methods utilize support vector machines (SVMs) and boosting for sparse crowds.…”

Section: Introductionmentioning

confidence: 99%

Cascaded parallel crowd counting network with multi-resolution collaborative representation

Lyu

Han

Chen³

2022

Appl Intell

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Accurately estimating the size and density distribution of a crowd from images is of great importance to public safety and crowd management during the COVID-19 pandemic, but it is very challenging as it is affected by many complex factors, including perspective distortion and background noise information. In this paper, we propose a novel multi-resolution collaborative representation framework called the cascaded parallel network (CP-Net), consisting of three parallel scale-specific branches connected in a cascading mode. In the framework, the three cascaded multi-resolution branches efficiently capture multi-scale features through their specific receptive fields. Additionally, multi-level feature fusion and information filtering are performed continuously on each branch to resist noise interference and perspective distortion. Moreover, we design an information exchange module across independent branches to refine the features extracted by each specific branch and deal with perspective distortion by using complementary information of multiple resolutions. To further improve the robustness of the network to scale variance and generate high-quality density maps, we construct a multi-receptive field fusion module to aggregate multi-scale features more comprehensively. The performance of our proposed CP-Net is verified on the challenging counting datasets (UCF_CC_50, UCF-QNRF, Shanghai Tech A&B, and WorldExpo’10), and the experimental results demonstrate the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Cascaded parallel crowd counting network with multi-resolution collaborative representation

Lyu

Han

Chen³

2022

Appl Intell

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“…Usually, the Gaussian kernel is generated with each head as the center, but it does not match the size of the head, and the density map is obviously interfered by the background [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

Ren

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Background. In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods. In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN “mid-term” fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results. MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion. MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.

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“…This is because instead of using a simple binary or one-hot output to predict the occupancy or person count, we estimate the occupancy density function. The concept is borrowed from the crowd counting domain [26], in which it is a solution preferred over explicit detection, as it avoids the dependence on the detector by learning the mapping of images to density maps [27]. The main contributions of the paper are:…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Kraft

Aszkowski²,

Pieczyński³

et al. 2021

Energies

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Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.

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Approaches on crowd counting and density estimation: a review

Cited by 50 publications

References 95 publications

Cascaded parallel crowd counting network with multi-resolution collaborative representation

Cascaded parallel crowd counting network with multi-resolution collaborative representation

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

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