Computer Science ›› 2020, Vol. 47 ›› Issue (4): 184-188.doi: 10.11896/jsjkx.190700212

• Artificial Intelligence • Previous Articles     Next Articles

Study of Crowd Counting Algorithm of “Weak Supervision” Dense Scene Based on DeepNeural Network

LIU Yan, LEI Yin-jie, NING Qian   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China
  • Received:2019-07-31 Online:2020-04-15 Published:2020-04-15
  • Contact: NING Qian,born in 1969,associate professor.Her main research interests include computer application and intelligent control.
  • About author:LIU Yan,born in 1995,postgraduate.His main research interests include deep learning and crowd counting.
  • Supported by:
    This work was supported by the Key Research and Development Program of Sichuan Province (2019YFG0409)

Abstract: At present,in the crowd counting task of dense scenes,the method of annotating true density is to annotate the central position of pedestrian’s head.Gaussian convolution is used to generate the ground-truth density map as the supervision information.However,for dense scenes,such labeling method is time-consuming and laborious,and there are many “uncontrolled” factors in the images of dense scenes,such as low resolution,background noise,heavy occlusion and scale change.To solve this problem,we proposed a new annotation method,that is,we only need to know how many persons are included in the picture,and the total count of pedestrians in the picture is used as the supervision information.Compared with the traditional real density map,in proposed labeling method,the real target value is used as the “weak supervision” information.The experimental results show that the model obtained by training neural network with weak supervisory information can accurately regress the number of targets in the image for crowd regression task,indicating the effectiveness of this method.

Key words: Deep learning, Neural network, Crowd counting, Weak supervision

CLC Number: 

  • TP391.413
[1]DALAL N,TRIGGS B.Histograms of Oriented Gradients for Human Detection[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Diego:IEEE Press,2005:886-893.
[2]VIOLA P,JONES M J.Robust Real-Time Face Detection[J] International Journal of Computer Vision,2004,57(2):137-154.
[3]FIASCHI L,KÖTHE U,NAIR R,et al.Learning to Count with Regression Forest and Sturctured Labels[C]// Proceedings of International Conference on Pattern Recognition.Tsukuba:IEEE Press,2012:2685-2688.
[4]CHAN A B,VASCONCELOS N.Bayesian Poisson Regressionfor Crowd Countin[C]//Proceedings of IEEE International Conference on Computer Vision.Tokyo:IEEE Press,2009.
[5]ZHANG Y,ZHOU D,et al.Single-image Crowd Counting via Multi-column Convolutional Neural Network[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.LAS VEGAS:IEEE Press,2016:589-597.
[6]SAM D B,SURYA S,BABU R V,et al.Switching Convolutional Neural Network for Crowd Counting[C]//Proceedigs of IEEE Conference on Computer Vision and Pattern Recognitio.Honolulu:IEEE Press,2017:5744-5752.
[7]SINDAGI V A,PATEL V M.Generating High-Quality Crowd Density Maps Using Contextual Pyramid Cnns[C]//Procee-dings of IEEE InternationalConferenceon Computer Vision.Ve-nice:IEEE Press,2017:1861-1870.
[8]LI Y,ZHANG X,CHEN D.Csrnet:Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake:IEEE Press,2018:1091-1100.
[9]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]// Proceedings of International Conference on Learning Representations.San Diego:IEEE Press,2015.
[10]LIU X,VAN DE WEIJER J,et al.Leveraging Unlabeled Data for Crowd Counting by Learning to rank[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake:IEEE Press,2018:7661-7669.
[11]WANG Q,GAO J,et al.Learning from Synthetic Data forCrowd Counting in The Wild[C]//Proceedings of IEEE Confe-rence on Computer Vision and Pattern Recognition Long.Beach:IEEE Press,2019.
[12]GOODFELLOW I,POUGET-ABADIE J,et al.Generative Adversarial Nets[C]//Proceedings of International Conference on Neural Information Processing Systems Lake.Tahoe:MIT Press,2014.
[13]SAM D B,SAJJAN N N,et al.Almost Unsupervised Learning for Dense Crowd Counting[C]//Proceedings of American Conference on Artificial Intelligence.Honolulu:AAAI Press,2019.
[14]KRIZHEVSKY A,SUTSKEVER I,et al.Imagenet Classification With Deep Convolutional Neural Networks[C]// Procee-dings of International Conference on Neural Information Proces-sing Systems.Montrea:MIT Press,2012:1097-1105.
[1] YU Xue-yong, CHEN Tao. Privacy Protection Offloading Algorithm Based on Virtual Mapping in Edge Computing Scene [J]. Computer Science, 2021, 48(1): 65-71.
[2] SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing. L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing [J]. Computer Science, 2021, 48(1): 89-95.
[3] HE Yan-hui, WU Gui-xing, WU Zhi-qiang. Domain Alignment Based Object Detection of X-ray Images [J]. Computer Science, 2021, 48(1): 175-181.
[4] LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min. Survey on Target Site Prediction of Human miRNA Based on Deep Learning [J]. Computer Science, 2021, 48(1): 209-216.
[5] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[6] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[7] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[8] ZHANG Yan-mei, LOU Yin-cheng. Deep Neural Network Based Ponzi Scheme Contract Detection Method [J]. Computer Science, 2021, 48(1): 273-279.
[9] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[10] ZHUANG Shi-jie, YU Zhi-yong, GUO Wen-zhong, HUANG Fang-wan. Short Term Load Forecasting via Zoneout-based Multi-time Scale Recurrent Neural Network [J]. Computer Science, 2020, 47(9): 105-109.
[11] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[12] ZHANG Jia-jia, ZHANG Xiao-hong. Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability [J]. Computer Science, 2020, 47(9): 129-134.
[13] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[14] ZHU Ling-ying, SANG Qing-bing, GU Ting-ting. No-reference Stereo Image Quality Assessment Based on Disparity Information [J]. Computer Science, 2020, 47(9): 150-156.
[15] ZHAO Qin-yan, LI Zong-min, LIU Yu-jie, LI Hua. Cascaded Siamese Network Visual Tracking Based on Information Entropy [J]. Computer Science, 2020, 47(9): 157-162.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .