An attentive hierarchy ConvNet for crowd counting in smart city

Zhai, Wenzhe; Gao, Mingliang; Souri, Alireza; Li, Qilei; Guo, Xiangyu; Shang, Jianrun; Zou, Guofeng

doi:10.1007/s10586-022-03749-2

Cited by 21 publications

(8 citation statements)

References 38 publications

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“…Sindagi et al [25] introduced the contextual pyramid CNN to estimate the crowd density and count by integrating global and local contextual information in crowd images. Similarly, Zhai et al [26] presented a novel framework for crowd counting. The framework employs a discriminative feature extractor to extract hierarchical features and utilizes a hierarchical fusion strategy to mine semantic features in a coarse-to-fine manner.…”

Section: Regression-based Methodsmentioning

confidence: 99%

Crowd Counting in Diverse Environments Using a Deep Routing Mechanism Informed by Crowd Density Levels

Alhawsawi,

Khan,

Ur Rehman

2024

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Automated crowd counting is a crucial aspect of surveillance, especially in the context of mass events attended by large populations. Traditional methods of manually counting the people attending an event are error-prone, necessitating the development of automated methods. Accurately estimating crowd counts across diverse scenes is challenging due to high variations in the sizes of human heads. Regression-based crowd-counting methods often overestimate counts in low-density situations, while detection-based models struggle in high-density scenarios to precisely detect the head. In this work, we propose a unified framework that integrates regression and detection models to estimate the crowd count in diverse scenes. Our approach leverages a routing strategy based on crowd density variations within an image. By classifying image patches into density levels and employing a Patch-Routing Module (PRM) for routing, the framework directs patches to either the Detection or Regression Network to estimate the crowd count. The proposed framework demonstrates superior performance across various datasets, showcasing its effectiveness in handling diverse scenes. By effectively integrating regression and detection models, our approach offers a comprehensive solution for accurate crowd counting in scenarios ranging from low-density to high-density situations.

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Section: Regression-based Methodsmentioning

confidence: 99%

Crowd Counting in Diverse Environments Using a Deep Routing Mechanism Informed by Crowd Density Levels

Alhawsawi,

Khan,

Ur Rehman

2024

Information

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“…Te efect of background interference is suppressed by Object Region Recognition module. Zhai et al [23] proposed Scale-Context Perceptive Network (SCPNet), which consists of Scale Perceptive (SP) module and Context Perceptive (CP) module. Te scale variation problem is solved by a local-global branching structure, and the CP module uses a channel-space self-attention mechanism to suppress the efect of background interference.…”

Section: Single-view Perception Methodsmentioning

confidence: 99%

“…Te scale variation problem is solved by a local-global branching structure, and the CP module uses a channel-space self-attention mechanism to suppress the efect of background interference. Zhai et al [24] proposed Attentive Hierarchy ConvNet (AHNet) in which discriminative feature extractor is used to extract multi-level feature representation and hierarchical feature aggregator is used to mine semantic features in a coarseto-fne manner. Zhai et al [25] proposed Feature Pyramid Attention Network (FPANet), which uses a lightweight structure to extract features at multiple scales and uses an attention mechanism to focus on crowd regions and suppress background interference.…”

Section: Single-view Perception Methodsmentioning

confidence: 99%

A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems

Zhang,

Ye,

et al. 2023

International Journal of Intelligent Systems

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Dense crowd counting has become an essential technology for urban security management. The traditional crowd counting methods mainly apply to the scene with a single view and obvious features but cannot solve the problem with a large area and fuzzy crowd features. Therefore, this paper proposes a crowd counting method based on high and low view information fusion (HLIF) for large and complex scenes. First, a neural network based on an attention mechanism (AMNet) is established to obtain a global density map from a high view and crowd counts from a low view. Then, the temporal correlation and spatial complementarity between cameras are used to calibrate the overlap areas of the two images. Finally, the total number of people is calculated by combining the low-view crowd counts and the high-view density map. Compared to single-view crowd counting methods, HLIF is experimentally more accurate and has been successfully applied in practice.

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“…Cai et al [46] proposed a network to learn the latent representations and subspace bases simultaneously, and the clustering outcomes can be inferred through the learned subspace bases. Zhai et al [47] extracted hierarchy features from single view images and designed an attentive module for each feature map to enhance feature representations. However, these approaches are mainly applied to single-view subspace clustering and difficult to keep a balance between accuracy and efficiency.…”

Section: Scalable Subspace Clusteringmentioning

confidence: 99%

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Shi,

Zhao

2023

Applied Sciences

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Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.

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An attentive hierarchy ConvNet for crowd counting in smart city

Cited by 21 publications

References 38 publications

Crowd Counting in Diverse Environments Using a Deep Routing Mechanism Informed by Crowd Density Levels

Crowd Counting in Diverse Environments Using a Deep Routing Mechanism Informed by Crowd Density Levels

A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

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