基于多层BP神经网络和无参数微调的人群计数方法

doi:10.11896／j.issn.1002-137X.2018.10.043

计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 235-239.doi: 10.11896／j.issn.1002-137X.2018.10.043

基于多层BP神经网络和无参数微调的人群计数方法

徐洋^1,2, 陈燚², 黄磊², 谢晓尧^1,2

贵州师范大学贵州省信息与计算科学重点实验室贵阳550001 ¹
贵州师范大学贵阳市公安局信息安全联合研究中心贵阳550001 ²

收稿日期:2017-03-17 出版日期:2018-11-05 发布日期:2018-11-05
作者简介:徐洋(1983-),男,博士后,副教授,硕士生导师,主要研究方向为机器学习、信息安全、大数据等,E-mail:xy@gznu.edu.cn(通信作者);陈燚(1992-),男,硕士,主要研究方向为机器学习、信息安全;黄磊(1981-),男,主要研究方向为网络安全;谢晓尧(1952-),男,博士,教授,博士生导师,主要研究方向为大数据、信息安全。
基金资助:
贵州省基础研究重大项目(黔科合J字20142001),贵州省科技合作计划重点项目(黔科合LH字20157763),住房和城乡建设部科学技术计划项目(2016-K3-009),全国统计科学研究项目(2016LY81)资助

Crowd Counting Method Based on Multilayer BP Neural Networks and Non-parameter Tuning

XU Yang^1,2, CHEN Yi², HUANG Lei², XIE Xiao-yao^1,2

Key Laboratory of Information and Computing Science of Guizhou Province,Guizhou Normal University,Guiyang 550001,China ¹
Guizhou Normal University & Guiyang Public Security Bureau Joint Research Centre for Information Security, Guizhou Normal University,Guiyang 550001,China ²

Received:2017-03-17 Online:2018-11-05 Published:2018-11-05

摘要/Abstract

摘要： 针对大部分现有的人群计数方法被应用到新的场景时性能下降的问题,在多层BP神经网络框架下,提出一种具有无参数微调的人群计数方法。首先,从训练图像中裁切图像块,将获得的相似尺度的行人作为人群BP神经网络模型的输入;然后,BP神经网络模型通过学习预测密度图,得到了一个具有代表性的人群块;最后,为了处理新场景,对训练好的BP神经网络模型进行目标场景微调,可追求有相同属性的样本,包括候选块检索和局部块检索。实验数据集包括PETS2009数据集、UCSD数据集和UCF_CC_50数据集。这些场景的实验结果验证了提出方法的有效性。相比于全局回归计数法和密度估计计数法,提出的方法在平均绝对误差和均方误差方面均有较大优势,消除了场景间区别和前景分割的影响。

关键词: BP神经网络, 密度图, 平均绝对误差, 人群计数, 无参数微调

Abstract: Because the performance of most existing crowd counting methods isdecreased when they are applied to a new scene,a crowd counting method based on non-parameter tuning was proposed in the framework of multilayer BP neural network.Firstly,image blocks are cropped from the training images to obtain similar scale pedestrian as an input of crowd BP neural network model.Then,the predictive density map is learned by BP neural network model to obtain representative crowd blocks.Finally,in order to deal with the new scene,the target scene is adjusted on the trained BP neural network model,retrieving samples with the same attributes,which includes candidate block retrieval and local block retrieval.The data set includes PETS2009 data set,UCSD data set and UCF_CC_50 data set.The effectiveness of the proposed method is verified by the experimental results on these scenes.Compared with the global regression coun-ting method and density estimation counting method,the proposed method has advantages of average absolute error and mean square error,and overcomes the influences of the differences between the scenes and foreground segmentation.

Key words: Average absolute error, BP neural network, Crowd counting, Density map, Non-parameter tuning

中图分类号:

TP301.6

徐洋, 陈燚, 黄磊, 谢晓尧. 基于多层BP神经网络和无参数微调的人群计数方法[J]. 计算机科学, 2018, 45(10): 235-239. https://doi.org/10.11896／j.issn.1002-137X.2018.10.043

XU Yang, CHEN Yi, HUANG Lei, XIE Xiao-yao. Crowd Counting Method Based on Multilayer BP Neural Networks and Non-parameter Tuning[J]. Computer Science, 2018, 45(10): 235-239. https://doi.org/10.11896／j.issn.1002-137X.2018.10.043

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基于多层BP神经网络和无参数微调的人群计数方法

Crowd Counting Method Based on Multilayer BP Neural Networks and Non-parameter Tuning

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