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Dynamic regional population counting and localization method based on high resolution fusion.

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Title: Dynamic regional population counting and localization method based on high resolution fusion.
Alternate Title: 基于高分辨率融合的动态区域人群计数定位方法.
Authors: Jiaojiao, ZHANG1, Yong, CHEN1,2
Source: Journal of Measurement Science & Instrumentation. Dec2025, Vol. 16 Issue 4, p515-525. 11p.
Subjects: Crowds, Feature extraction, Artificial neural networks, Forecasting, Computer vision
Abstract (English): Aiming at the problem of inaccurate crowd counting and location in dense scenes, a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed. Firstly, U-HRNet was used as the main backbone to extract highresolution features of the population and enhance the ability of feature extraction with different resolutions. Then, the dynamic regional awareness attention module was designed to make full use of the global and local feature information, refine the differentiated learning of target feature and background feature, reduce the interference of background feature, and improve the positioning performance of the model. Finally, the predicted threshold map and confidence map were input into the binarization module to output the prediction and counting results of the crowd independent individual target. Experimental results showed that the proposed method achieved good performance of counting and positioning in different scenarios. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对密集场景下现有人群计数方法存在计数与定位不准确的问题, 提出了一种基于高分辨率融合的动态区域感知人群计 数与定位方法。首先, 采用 U -HRNet 为主干网络, 提取人群高分辨率特征, 增强不同分辨率特征提取能力。然后, 设计动态区域 感知注意力模块, 充分利用全局与局部特征信息, 细化目标特征和背景特征的差异化学习, 降低背景对人群计数的干扰, 提高模型 定位性能。最后, 将预测的阈值图和置信图输入到二值化模块中, 输出人群计数结果。实验结果表明, 所提方法在不同场景下的 计数与定位都取得了更好的表现。 [ABSTRACT FROM AUTHOR]
Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 190833611
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PubType: Academic Journal
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Dynamic regional population counting and localization method based on high resolution fusion.
– Name: TitleAlt
  Label: Alternate Title
  Group: TiAlt
  Data: 基于高分辨率融合的动态区域人群计数定位方法.
– Name: Author
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jiaojiao%2C+ZHANG%22">Jiaojiao, ZHANG</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yong%2C+CHEN%22">Yong, CHEN</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Measurement+Science+%26+Instrumentation%22">Journal of Measurement Science & Instrumentation</searchLink>. Dec2025, Vol. 16 Issue 4, p515-525. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Crowds%22">Crowds</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Aiming at the problem of inaccurate crowd counting and location in dense scenes, a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed. Firstly, U-HRNet was used as the main backbone to extract highresolution features of the population and enhance the ability of feature extraction with different resolutions. Then, the dynamic regional awareness attention module was designed to make full use of the global and local feature information, refine the differentiated learning of target feature and background feature, reduce the interference of background feature, and improve the positioning performance of the model. Finally, the predicted threshold map and confidence map were input into the binarization module to output the prediction and counting results of the crowd independent individual target. Experimental results showed that the proposed method achieved good performance of counting and positioning in different scenarios. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 针对密集场景下现有人群计数方法存在计数与定位不准确的问题, 提出了一种基于高分辨率融合的动态区域感知人群计 数与定位方法。首先, 采用 U -HRNet 为主干网络, 提取人群高分辨率特征, 增强不同分辨率特征提取能力。然后, 设计动态区域 感知注意力模块, 充分利用全局与局部特征信息, 细化目标特征和背景特征的差异化学习, 降低背景对人群计数的干扰, 提高模型 定位性能。最后, 将预测的阈值图和置信图输入到二值化模块中, 输出人群计数结果。实验结果表明, 所提方法在不同场景下的 计数与定位都取得了更好的表现。 [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.62756/jmsi.1674-8042.2025050
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 515
    Subjects:
      – SubjectFull: Crowds
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Computer vision
        Type: general
    Titles:
      – TitleFull: Dynamic regional population counting and localization method based on high resolution fusion.
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            NameFull: Jiaojiao, ZHANG
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            NameFull: Yong, CHEN
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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