Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

Zhang, Jun; Zhu, Gaoyi; Wang, Zhizhong

doi:10.3390/sym12040682

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…They can be used in a variety of scenarios, such as political assemblies, sports events, and concerts, to ensure public safety by monitoring crowd density. Currently available methods of crowd counting are developed from detection-based [4,[6][7][8] and regression-based [5,9,10] approaches and convolutional neural network (CNN)based [11][12][13][14][15][16] approaches. As CNN-based methods use the human head as the target of detection, the error caused by occlusion is reduced.…”

Section: R Peer Reviewmentioning

confidence: 99%

Convolutional Neural Network for Crowd Counting on Metro Platforms

2021

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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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Section: R Peer Reviewmentioning

confidence: 99%

Convolutional Neural Network for Crowd Counting on Metro Platforms

2021

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“…However, the method can only divide the image into two fxed regions without considering the variability of the scene. Te MPCNet proposed by Zhang et al [22] uses multicolumn dilated convolution to aggregate multiscale context information in crowded scenes, but the multicolumn structure inference speed is slow and cannot meet the requirements of real-time detection. Tiny MetroNet proposed by Guo et al [23] adopts a micro-passenger feature extraction network as the backbone network to achieve a balance between counting accuracy and detection speed.…”

Section: Introductionmentioning

confidence: 99%

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Zuo,

Liu,

2023

Journal of Advanced Transportation

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Accurate access to real-time passenger flows on subway platforms helps to refine management in the era of networked operations. The narrow subway platforms suffer from significant crowd scale discrepancies and complex backgrounds when counting passenger flow. In the proposed passenger flow counting algorithm, the feature-enhanced pyramid structure is used to retain the channel information of deep features and eliminate the aliasing effect caused by fusion to enhance the feature representation of the original image and effectively solve the scale problem. The mixed attention mechanism suppresses background interference by capturing the global context relationship and focusing on the target area. On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. The MAE and MSE on the self-built platform dataset reach 3.1 and 5.7, respectively. The experimental results show that the accuracy of the proposed algorithm is improved and can meet the counting requirements of the subway platform scene.

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“…The deep learningbased methods have greatly improved the accuracy of passenger flow detection and reduced the influence of environmental changes on the performance of models. Zhang et al [5] proposed MPCNet, which uses CNN to extract features and then uses a multi-column atrous CNN with atrous spatial pyramid pooling to estimate the crowd size. It can aggregate multi-scale contextual information in crowded scenes.…”

Section: Introductionmentioning

confidence: 99%

MPDNet: A Transformer-based real-time passenger detection network in metro stations

et al. 2022

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We propose a passenger flow detection method for dense areas of subway stations to address the current situation that existing pedestrian detection models cannot meet the real-time performance requirements in subway applications and lack validation in multiple subway scenarios. First, we designed the MPDNet model, which uses PVT-small to extract features and an improved feature pyramid network (FPN) for upsampling using the adaptively spatial feature fusion (ASFF) algorithm to retain more local information in the output of the FPN. Second, to better evaluate the performance of models in the metro, we collected subway surveillance video data and proposed the MetroStation dataset. Finally, we trained and evaluated the performance of the MPDNet model on the MetroStation dataset. We compare our method with several common object detection models on the MetroStation dataset, using mAP and frames per second (FPS) to verify its accuracy. The experiments on the MetroStation dataset demonstrated that the MPDNet performed well and satisfied inference speed requirements in metro passenger flow detection.

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Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

Cited by 6 publications

References 30 publications

Convolutional Neural Network for Crowd Counting on Metro Platforms

Convolutional Neural Network for Crowd Counting on Metro Platforms

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

MPDNet: A Transformer-based real-time passenger detection network in metro stations

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