GB2614806A - Method of crowd density estimation based on multi-scale feature fusion of residual network - Google Patents

Method of crowd density estimation based on multi-scale feature fusion of residual network Download PDF

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GB2614806A
GB2614806A GB2217424.7A GB202217424A GB2614806A GB 2614806 A GB2614806 A GB 2614806A GB 202217424 A GB202217424 A GB 202217424A GB 2614806 A GB2614806 A GB 2614806A
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map
feature map
feature
image
network
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GB202217424D0 (en
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He Xianding
Deng Lijia
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Chengdu Aeronautic Polytechnic
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Chengdu Aeronautic Polytechnic
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
GB2217424.7A 2021-11-22 2022-11-22 Method of crowd density estimation based on multi-scale feature fusion of residual network Pending GB2614806A (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111384302.XA CN113807334B (zh) 2021-11-22 2021-11-22 一种基于残差网络的多尺度特征融合的人群密度估计方法

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GB202217424D0 GB202217424D0 (en) 2023-01-04
GB2614806A true GB2614806A (en) 2023-07-19

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CN116944818B (zh) * 2023-06-21 2024-05-24 台州必拓汽车配件股份有限公司 新能源汽车转轴的智能加工方法及其系统
CN116883360B (zh) * 2023-07-11 2024-01-26 大连海洋大学 一种基于多尺度双通道的鱼群计数方法
CN117739289B (zh) * 2024-02-20 2024-04-26 齐鲁工业大学(山东省科学院) 基于声图融合的泄漏检测方法及系统

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CN106778502B (zh) * 2016-11-21 2020-09-22 华南理工大学 一种基于深度残差网络的人群计数方法
CN109241895B (zh) * 2018-08-28 2021-06-04 北京航空航天大学 密集人群计数方法及装置
CN109460855A (zh) * 2018-09-29 2019-03-12 中山大学 一种基于聚焦机制的群体流量预测模型及方法
CN110020606B (zh) * 2019-03-13 2021-03-30 北京工业大学 一种基于多尺度卷积神经网络的人群密度估计方法
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CN112101164A (zh) * 2020-09-06 2020-12-18 西北工业大学 基于全卷积网络的轻量化人群计数方法
CN112861718A (zh) * 2021-02-08 2021-05-28 暨南大学 一种轻量级特征融合人群计数方法及系统
CN112597985B (zh) * 2021-03-04 2021-07-02 成都西交智汇大数据科技有限公司 一种基于多尺度特征融合的人群计数方法
CN113139489B (zh) * 2021-04-30 2023-09-05 广州大学 基于背景提取和多尺度融合网络的人群计数方法及系统

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CN113807334A (zh) 2021-12-17
CN113807334B (zh) 2022-02-18

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