GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting

Shi, Ying; Sang, Jun; Tan, Jinghan; Wu, Zhongyuan; Cai, Bin; Sang, Nong

doi:10.1007/978-3-030-86340-1_5

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…When the size of the crowd exceeds a few hundred people, the detection model is difficult to cope with due to the small size and serious occlusion. In contrast, the regression method based on the density map can more reliably obtain the overall characteristics of the crowd and can effectively estimate the number of the crowd [ 9 , 13 , 14 ].…”

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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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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“…e method based on direct regression can estimate the number of people from the scene image using the linear regression function [19,20]. Indirect regression regresses the population density map from the input image [21,22] and then integrates the density map to predict the population number.…”

Section: Introductionmentioning

confidence: 99%

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.

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GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting

Cited by 2 publications

References 19 publications

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

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

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

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