可伸缩模块化CNN人群计数方法

doi:10.11896/j.issn.1002-137X.2018.08.004

计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 17-21.doi: 10.11896/j.issn.1002-137X.2018.08.004

• 2017 中国多媒体大会 • 上一篇下一篇

可伸缩模块化CNN人群计数方法

李云波¹, 唐斯琪¹, 周星宇², 潘志松¹

中国人民解放军理工大学指挥信息系统学院南京210000¹
中国人民解放军理工大学通信工程学院南京210000²

收稿日期:2017-10-24 出版日期:2018-08-29 发布日期:2018-08-29
作者简介:李云波(1994-),男,硕士,主要研究方向为机器学习、深度学习,E-mail:18252059269@163.com; 唐斯琪(1993-),女,硕士,主要研究方向为深度学习、图像视觉; 周星宇(1985-),男,硕士,讲师,主要研究方向为机器学习、模式识别; 潘志松(1973-),男,博士,教授,主要研究方向为模式识别与人工智能,E-mail:panzs@nuaa.edu.cn(通信作者)。
基金资助:
本文受属性学习及其应用研究(61473149),2017年国家重点研发计划“网络空间安全”重点专项(2017YFB0802800)资助。

Crowd Counting Method via Scalable Modularized Convolutional Neural Network

LI Yun-bo¹, TANG Si-qi¹, ZHOU Xing-yu², PAN Zhi-song¹

Institute of Command Information System,PLA University of Science and Technology,Nanjing 210000,China¹
College of Communication Engineering,PLA University of Science and Technology,Nanjing 210000,China²

Received:2017-10-24 Online:2018-08-29 Published:2018-08-29

摘要/Abstract

摘要： 本文目标是根据任意视角、任意人群密度的图像信息,估计真实场景中的人群密度。但三维空间景物投影到二维空间时会造成透视失真和人群遮挡问题,导致难以区分个体与个体、个体与背景的差异。为此,提出一种灵活高效的可伸缩模块化卷积神经网络(CNN)的架构,允许直接输入任意大小和分辨率的图像,不额外计算视角变化信息,通过生成密度图的方式来估计人群数量。架构的每个模块采用不同卷积核的多列结构,可以拟合不同远近的个体信息;并结合前后两层的特征信息,减少了梯度消失造成的精度下降损失。实验证明,在ShanghaiTech PartA和PartB数据集上,所提方法的准确率比之前最好的MCNN方法分别提高了14.58%,40.53%,均方根误差分别降低了23.89%,33.90%。

关键词: 人群计数, 卷积神经网络, 可伸缩模块, 密度图, 特征融合

Abstract: The purpose of this paper is to accurately estimate the crowd density in real scenes based on image information from arbitrary perspective and arbitrary crowd density.However,crowd counting on static images is a challenging problem.Due to the perspective distortion and the crowd crushes caused by the projection from 3D space into 2D space,it is difficult to distinguish the difference between individual and individual and the difference between individual and background.To this end,this paper proposed a flexible and efficient scalable modularized convolutional neural network (CNN) architecture.The network allows to directly input images with arbitrary size and resolution and it does not require additional computational changes in view information.Each module of the architecture employs a multiple column structure with different convolution kernels,which can be used to fit individual information of different distances.The proposed module also combines the feature information of the front and rear two layers,reducing the decrease loss of the accuracy caused by the vanishing of the gradient.Experiments show thatthe accuracy of proposed method is increased by 14.58% and 40.53%,and the root mean square error is reduced by 23.89% and 33.90% respectively on ShanghaiTech PartA and PartB datasets compared with the state-of-the-art MCNN methods.

Key words: Crowd counting, Convolutional neural network, Scalable module, Density maps, Feature fusion

中图分类号:

TP391

李云波, 唐斯琪, 周星宇, 潘志松. 可伸缩模块化CNN人群计数方法[J]. 计算机科学, 2018, 45(8): 17-21.

LI Yun-bo, TANG Si-qi, ZHOU Xing-yu, PAN Zhi-song. Crowd Counting Method via Scalable Modularized Convolutional Neural Network[J]. Computer Science, 2018, 45(8): 17-21.

参考文献

[1]LIN S F,CHEN J Y,CHAO H X.Estimation of number of people in crowded scenes using perspective transformation[J].IEEE Transactions on Systems,Man & Cybernetics Part A Systems & Humans,2001,31(6):645-654.
[2]DALAL N,TRIGGS B.Histograms of Oriented Gradients for Human Detection[C]∥IEEE Computer Society Conference on Computer Vision & Pattern Recognition.IEEE Computer Society,2005:886-893.
[3]WANG M,WANG X.Automatic adaptation of a generic pedestrian detector to a specific traffic scene[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2011:3401-3408.
[4]GE W,COLLINS R T.Marked point processes for crowd-coun-ting[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009(CVPR 2009).IEEE,2009:2913-2920.
[5]IDREES H,SOOMRO K,SHAH M.Detecting Humans inDense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning[M].IEEE Computer Society,2015.
[6]LIN Z,DAVIS L S.Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(4):604-618.
[7]LEMPITSKY V S,ZISSERMAN A.Learning To Count Objects in Images[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2010:1324-1332.
[8]ZHANG C,LI H,WANG X,et al.Cross-scene crowd counting via deep convolutional neural networks[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2015:833-841.
[9]WANG C,ZHANG H,YANG L,et al.Deep People Counting in Extremely Dense Crowds[C]∥ACM International Conference on Multimedia.ACM,2015:1299-1302.
[10]BOOMINATHAN L,KRUTHIVENTI S S S,BABU R V.CrowdNet:A Deep Convolutional Network for Dense Crowd Counting[C]∥Proceedings of ACM Conference on Multimedia (ACMMM) - 2016.2016:640-644.
[11]ZHANG Y,ZHOU D,CHEN S,et al.Single-Image CrowdCounting via Multi-Column Convolutional Neural Network[C]∥Computer Vision and Pattern Recognition.IEEE,2016:589-597.
[12]HAN S,POOL J,TRAN J,et al.Learning both Weights and Connections for Efficient Neural Networks[C]∥NIPS 2015.2015:1135-1143.
[13]HAN S,LIU X,MAO H,et al.EIE:Efficient Inference Engine on Compressed Deep Neural Network[C]∥ACM/IEEE International Symposium on Computer Architecture.IEEE,2016:243-254.
[14]HAN S,MAO H,DALLY W J.Deep Compression:Compressing Deep Neural Networks with Pruning,Trained Quantization and Huffman Coding[J].Fiber,2015,56(4):3-7.
[15]LIN M,CHEN Q,YAN S.Network In Network[C]∥International Conference on Learning Representations.2013.
[16]NAIR V,HINTON G E.Rectified linear units improve restric-ted boltzmann machines[C]∥International Conference on International Conference on Machine Learning.Omnipress,2010:807-814.
[17]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima-ge Recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778.
[18]RODRIGUEZ M,LAPTEV I,SIVIC J,et al.Density-aware person detection and tracking in crowds[C]∥International Confe-rence on Computer Vision.IEEE Computer Society,2011:2423-2430.
[19]IDREES H,SALEEMI I,SEIBERT C,et al.Multi-source Multi-scale Counting in Extremely Dense Crowd Images[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2013:2547-2554.
[20]OÑORO-RUBIO D,LÓPEZ-SASTRE R J.Towards Perspec-tive-Free Object Counting with Deep Learning[C]∥European Conference on Computer Vision.Springer,Cham,2016:615-629.

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可伸缩模块化CNN人群计数方法

Crowd Counting Method via Scalable Modularized Convolutional Neural Network

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