计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 297-303.doi: 10.11896/jsjkx.191100506C
丁亚三, 郭斌, 辛通, 王沛, 王柱, 於志文
DING Ya-san, GUO Bin, XIN Tong, WANG Pei, WANG Zhu, YU Zhi-wen
摘要: 人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴设备的人数识别方法,但是这些方案均存在一些不足,例如摄像头只能提供可视范围内的监控,可穿戴设备需要被监控对象有意识地穿戴。也有一些学者利用雷达相关技术实现了穿墙式感知识别,但是这类系统设计复杂,应用成本较高,多用于军事领域。文中提出了一种基于WiFi信号的室内人数识别方案WiCount,其利用信道状态信息(Channel State Information,CSI)的幅值波动来刻画室内人数的变化,利用机器学习算法实现对人的计数。WiCount旨在进行更细粒度的室内人数识别,即人在室内任意位置时该方法均能准确识别人数。它根据室内人数与CSI幅值变化的关系,提取了有效的数学特征,减弱了相同人数在室内不同位置所产生的CSI幅值波动差异,然后通过训练3种分类器(SVM、KNN、BP神经网络)来识别监测区域内的人数。在实验室和会议室分别部署了验证系统,结果显示,在人数规模较小的情况下,所提方法的识别效果良好。其中,实验室环境下,不超过4人时,系统的识别率达90%;会议室环境下,不超过2人,在监测区域内任意位置活动时,系统的识别率可达89.58%。
中图分类号:
[1]WEPPNER J,LUKOWICZ P.Collaborative Crowd Density Estimation with Mobile Phones[C]∥2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).IEEE,2012:193-200. [2]LI M,ZHANG Z,HUANG K,et al.Estimating the number ofpeople in crowded scenes by MID based foreground segmentation and head-shoulder detection[C]∥International Conference on Pattern Recognition.IEEE,2009:1-4. [3]CHAN A B,LIANG Z S J,VASCONCELOS N.Privacy preserving crowd monitoring:Counting people without people models or tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2008).IEEE,2008:1-7. [4]IDREES H,SALEEMI I,SEIBERT C,et al.Multi-sourceMulti-scale Counting in Extremely Dense Crowd Images[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2013:2547-2554. [5]SUBBURAMAN V B,DESCAMPS A,CARIN-COTTE C.Counting People in the Crowd Using a Generic Head Detector[C]∥IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.IEEE,2012:470-475. [6]LIYANAGE M,CHANG C,SRIRAMA S N.mePaaS:Mobile-Embedded Platform as a Service for Distributing Fog Computing to Edge Nodes[C]∥International Conference on Parallel and Distributed Computing,Applications and Technologies.IEEE,2017:73-80. [7]KANNAN P G,VENKATAGIRI S P,CHAN M C,et al.Lowcost crowd counting using audio tones[C]∥ACM Conference on Embedded Network Sensor Systems.ACM,2012:155-168. [8]VERSICHELE M,NEUTENS T,DELAFONT-AINE M,et al.The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events:a case study of the Ghent Festivities[J].Applied Geography,2012,32(2):208-220. [9]WEPPNER J,LUKOWICZ P.Bluetooth based CollaborativeCrowd Density Estimation with Mobil-e Phones[C]∥IEEE International Conference on Pervasive Computing and Communications (PerCom).IEEE,2013:193-200. [10]NAKATSUKA M,IWATANI H,KATTO J.A study on passive crowd density estimation using wireless sensors[C]∥The 4th Intl.Conf.on Mobile Computing and Ubiquitous Networking (ICMU 2008).IEEE,2008:54-59. [11]SCHAUER L,WERNER M,MARCUS P.Es-timating Crowd Densities and Pedestrian Flows Using Wi-Fi and Bluetooth[C]∥International Conference on Mobile and Ubiquitous Systems:Computing,Networking and Services.ACM,2014:171-177. [12]DING H,HAN J,LIU A X,et al.Human object estimation via backscattered radio frequency signal[C]∥Computer Communications.IEEE,2015:1652-1660. [13]YUAN Y,ZHAO J,QIU C,et al.Estimating Crowd Density in an RF-Based Dynamic Environment[J].IEEE Sensors Journal,2013,13(10):3837-3845. [14]FADHLULLAH S Y,ISMAIL W.A Statistical Approach inDesigning an RF-Based Human Crowd Density Estimation System[J].International Journal of Distributed Sensor Networks,2016,12:1-9. [15]MOHAN L,CHII C,SATISH N S,et al.Indoor People Density Sensing using Wi-Fi and Channel State Information[C]∥ACM 14th EAI International Conference on Mobile and Ubiquitous Systems:Computing,Networking and Services.ACM,2017:37-47. [16]DOMENICO S D,SANCTIS M D,CIANCA E,et al.A trainedonce crowd counting method using differential WiFi channel state information[C]∥Proceedings of the 3rd International on Workshop on Physical Analytics.ACM,2016:37-42. [17]XI W,ZHAO J,LI X Y,et al.Electronic frog eye:Countingcrowd using WiFi[C]∥IEEE INFOCOM 2014-IEEE Confe-rence on Computer Communications.IEEE,2014:361-369. [18]YOSHIDA T,TANIGU-CHI Y.Estimating the number of people using existing WiFi access point in indoor environment[C]∥Proceedings of the 6th European Conference of Computer Science (ECCS’15).2015:46-53. [19]YANG Z,ZHOU Z,LIU Y.From RSSI to CSI:Indoor localization via channel response[J].ACM Computing Surveys(CSUR),2013,46(2):25-25. [20]HALPERIN D,HU W,SHETH A,et al.Tool release:Gathering 802.11n traces with channel state information[J].ACM SIGCOMM Computer Communication Review,2011,41(1):53-53. [21]SEN S,LEE J,KIM K H,et al.Avoiding multipath to revive inbuilding Wi-Fi localization[C]∥Proceeding of the 11th Annual International Conference on Mobile Systems,Applications,and Services.ACM,2013:249-262. [22]MOHAMMADMORADI H,YIN S,GNAWA-LI O.Room Occupancy Estimation Through WiFi,UWB,and Light Sensors Mounted on Doorways[C]∥Proceedings of the 2017 International Conference on Smart Digital Environment.ACM,2017:27-34. [23]CHENG Y K,CHANG R Y.Device-Free Indoor People Counting Using Wi-Fi Channel State Information for Internet of Things[C]∥2017 IEEE Global Communications Conference(GLOBECOM 2017).IEEE,2017:1-6. [24]GONG L,YANG W,ZHOU Z,et al.An adaptive wireless passive human detection via fine-grained physical layer information[J].Ad Hoc Networks,2016,38(C):38-50. [25]HALPERIN D,HU W,SHETH A,et al.Tool release:gathering 802.11n traces with channel state information[J].Acm Sigcomm Computer Communication Review,2011,41(1):53-53. |
[1] | 冷典典, 杜鹏, 陈建廷, 向阳. 面向自动化集装箱码头的AGV行驶时间估计 Automated Container Terminal Oriented Travel Time Estimation of AGV 计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028 |
[2] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
[3] | 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094 |
[4] | 张光华, 高天娇, 陈振国, 于乃文. 基于N-Gram静态分析技术的恶意软件分类研究 Study on Malware Classification Based on N-Gram Static Analysis Technology 计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203 |
[5] | 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇. 基于大数据的进化网络影响力分析研究综述 Survey of Influence Analysis of Evolutionary Network Based on Big Data 计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240 |
[6] | 陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079 |
[7] | 李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述 Survey on Bayesian Optimization Methods for Hyper-parameter Tuning 计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208 |
[8] | 赵璐, 袁立明, 郝琨. 多示例学习算法综述 Review of Multi-instance Learning Algorithms 计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047 |
[9] | 卿朝进, 杜艳红, 叶青, 杨娜, 张岷涛. 存在CSI估计错误的增强型ELM叠加CSI反馈方法 Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors 计算机科学, 2022, 49(6A): 632-638. https://doi.org/10.11896/jsjkx.210800036 |
[10] | 王飞, 黄涛, 杨晔. 基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究 Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion 计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030 |
[11] | 肖治鸿, 韩晔彤, 邹永攀. 基于多源数据和逻辑推理的行为识别技术研究 Study on Activity Recognition Based on Multi-source Data and Logical Reasoning 计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270 |
[12] | 姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮. 一种基于异质模型融合的 Android 终端恶意软件检测方法 Android Malware Detection Method Based on Heterogeneous Model Fusion 计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103 |
[13] | 许杰, 祝玉坤, 邢春晓. 机器学习在金融资产定价中的应用研究综述 Application of Machine Learning in Financial Asset Pricing:A Review 计算机科学, 2022, 49(6): 276-286. https://doi.org/10.11896/jsjkx.210900127 |
[14] | 李野, 陈松灿. 基于物理信息的神经网络:最新进展与展望 Physics-informed Neural Networks:Recent Advances and Prospects 计算机科学, 2022, 49(4): 254-262. https://doi.org/10.11896/jsjkx.210500158 |
[15] | 么晓明, 丁世昌, 赵涛, 黄宏, 罗家德, 傅晓明. 大数据驱动的社会经济地位分析研究综述 Big Data-driven Based Socioeconomic Status Analysis:A Survey 计算机科学, 2022, 49(4): 80-87. https://doi.org/10.11896/jsjkx.211100014 |
|