计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 11-15.

• 综述研究 • 上一篇    下一篇

视觉群智感知应用综述

翟书颖1, 李茹1, 李波1, 郝少阳2   

  1. 西北工业大学明德学院 西安7101241;
    西北工业大学计算机学院 西安7101292
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 翟书颖(1981-),女,硕士,讲师,主要研究方向为物联网、群智感知等,E-mail:zhaisy@npumd.edu.cn
  • 作者简介:李 茹(1987-),女,硕士生,主要研究方向为自适应信号处理;李 波(1974-),男,博士,副教授,主要研究方向为无线通信和信号处理等;郝少阳(1995-),男,硕士生,主要研究方向为群智感知。
  • 基金资助:
    本文受陕西省教育厅专项科研计划项目(18JK1169),西北工业大学明德学院科研基金(2017XY02L01)资助。

Survey on Applications of Visual Crowdsensing

ZHAI Shu-ying1, LI Ru1, LI Bo1, HAO Shao-yang2   

  1. Mingde College,Northwestern Polytechnical University,Xi'an 710124,China1;
    School of Computer Science,Northwestern Polytechnical University,Xi'an 710129,China2
  • Online:2019-06-14 Published:2019-07-02

摘要: 近年来,通过图片、视频等进行感知的视觉群智感知已经成为移动群智感知的主要方式,是当前的研究热点之一。视觉群智感知要求用户以图片或者视频的形式获取真实世界中感知对象的细节信息,在各个领域都有较为广泛的应用。但是国内目前还没有文章对视觉群智感知的发展与现状进行总结。鉴于这种情况,文中综述了视觉群智感知的最新应用,包括平面图生成、室内场景重建、室外场景重建、事件重构、室内定位、室内导航、灾难救援和城市感知等;并对视觉群智感知目前面临的一些独特问题进行了总结。

关键词: 事件重构, 视觉群智感知, 室内导航, 室内定位, 移动群智感知

Abstract: In recent years,Visual Crowdsensing(VCS) that sensed through images and video,has become a predominant sensing paradigm of Mobile Crowdsensing(MCS),which is one of the current research hotspots.VCS requires people to capture the details of sensing objects in the real world in the form of pictures or video,which is widely used in various fields.However,there is no article summarizing the development and current situation of VCS in China.To this end,this paper summarized the latest applications of VCS,including floor plan generation,indoor scene reconstruction,outdoor scene reconstruction,event reconstruction,indoor localization,indoor navigation and disaster relief,and summarized some unique problems of VCS at present.

Key words: Event reconstruction, Indoor localization, Indoor navigation, Mobile crowdsensing, Visual crowdsensing

中图分类号: 

  • TP391
[1]GUO B,WANG Z,YU Z,et al.Mobile Crowd Sensing and Computing:The Review of an Emerging Human-Powered Sensing Paradigm[J].Acm Computing Surveys,2015,48(1):7.
[2]WANG L,ZHANG D,WANG Y,et al.Sparse mobile crow-dsensing:challenges and opportunities[J].IEEE Communications Magazine,2016,54(7):161-167.
[3]GUO B,HAN Q,CHEN H,et al.The Emergence of Visual Crowdsensing:Challenges and Opportunities[J].IEEE Communications Surveys & Tutorials,2017,PP(99):1.
[4]TENG X,GUO D,GUO Y,et al.SISE:Self-updating of Indoor Semantic Floorplans for General Entities[J].IEEE Transactions on Mobile Computing,2018,PP(99):1.
[5]ZHANG Q,ZHANG Q,SHI W,et al.Firework:Data Proces-sing and Sharing for Hybrid Cloud-Edge Analytics[J].IEEE Transactions on Parallel & Distributed Systems,2018,PP(99):1.
[6]TENG X,GUO D,GUO Y,et al.IONavi:an indoor-outdoor navigation service via mobile crowdsensing[J].ACM Transactions on Sensor Networks,2017,13(2):12.
[7]ZUO P,HUA Y,LIU X,et al.BEES:Bandwidth- and Energy- Efficient Image Sharing for Real-Time Situation Awareness[C]∥IEEE,International Conference on Distributed Computing Systems.IEEE,2017:1510-1520.
[8]CHEN S,LI M,REN K,et al.Crowd Map:Accurate Recon-struction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos∥2015 IEEE 35th International Conference on Distributed Computing Systems.IEEE,2015:1-10.
[9]GAO R,ZHAO M,YE T,et al.Jigsaw:indoor floor plan reconstruction via mobile crowdsensing[C]∥International Confe-rence on Mobile Computing and NETWORKING.ACM,2014:249-260.
[10]SANKAR A,SEITZ S.Capturing indoor scenes with smartphones[C]∥ACM Symposium on User Interface Software and Technology.ACM,2012:403-412.
[11]PENG Z,GAO S,XIAO B,et al.CrowdGIS:Updating Digital Maps via Mobile Crowdsensing[J].IEEE Transactions on Automation Science & Engineering,2017,PP(99):1-12.
[12]TUITE K,SNAVELY N,HSIAO D Y,et al.PhotoCity:train-ing experts at large-scale image acquisition through a competitive game∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.ACM,2011:1383-1392.
[13]RAYCHOUDHURY V,SHRIVASTAV S,SANDHA S S,et al. CROWD-PAN-360:Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users[J].IEEE Transactions on Parallel & Distributed Systems,2015,26(8):2208-2219.
[14]KIM S H,LU Y,SHI J,et al.Key Frame Selection Algorithms for Automatic Generation of Panoramic Images from Crow-dsourced Geo-tagged Videos[C]∥International Symposium on Web and Wireless Geographical Information Systems.Springer,Berlin,Heidelberg,2014:67-84.
[15]BAO X,CHOUDHURY R R.MoVi:mobile phone based video highlights via collaborative sensing[C]∥International Confe-rence on Mobile Systems,Applications,and Services.ACM,2010:357-370.
[16]GIRIDHAR P,WANG S,ABDELZAHER T,et al.On localizing urban events with instagram[C]∥INFOCOM 2017-IEEE Conference on Computer Communications.IEEE,2017:1-9.
[17]BANO S,CAVALLARO A.Discovery and organization of multi-camera user-generated videos of the same event[J].Information Sciences,2015,302:108-121.
[18]CARLIER A,CALVET L,GURDJOS P,et al.Querying Multiple Simultaneous Video Streams with 3D Interest Maps[M]∥Visual Content Indexing and Retrieval with Psycho-Visual Mo-dels.Springer,Cham,2017:125-144.
[19]RODRIGUES J,MARQUES E R B,SILVA J,et al.Video Dissemination in Untethered Edge-Clouds:A Case Study[C]∥IFIP International Conference on Distributed Applications and Interoperable Systems.Springer,Cham,2018:137-152.
[20]BOHEZ S,DANEELS G,VAN HERZEELE L,et al.The crowd as a cameraman:on-stage display of crowdsourced mobile video at large-scale events[J].Multimedia Tools and Applications,2018,77(1):597-629.
[21]XU H,YANG Z,ZHOU Z,et al.Enhancing wifi-based localization with visual clues[C]∥Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing.ACM,2015:963-974.
[22]CHON Y,LANE N D,LI F,et al.Automatically characterizing places with opportunistic crowdsensing using smartphones[C]∥Proceedings of the 2012 ACM Conference on Ubiquitous Computing.ACM,2012:481-490.
[23]DONG J,XIAO Y,NOREIKIS M,et al.iMoon:Using Smart-phones for Image-based Indoor Navigation[C]∥ACM Con-ference on Embedded Networked Sensor Systems.ACM,2015:85-97.
[24]https://mspg.azurewebsites.net.
[25]ROY Q,PERRAULT S T,DAVIS R C,et al.Follow-My-Lead:Intuitive Indoor Path Creation and Navigation Using Interactive Videos[C]∥CHI Conference on Human Factors in Computing Systems.ACM,2017:5703-5715.
[26]YIN Z,WU C,YANG Z,et al.Peer-to-peer indoor navigation using smartphones[J].IEEE Journal on Selected Areas in Communications,2017,35(5):1141-1153.
[27]SHU Y,SHIN K G,HE T,et al.Last-mile navigation using sma-rtphones[C]∥Proceedings of the 21st Annual International Conference on Mobile Computing and Networking.ACM,2015:512-524.
[28]RICHERZHAGEN B,WULFHEIDE J,KOEPPL H,et al.Enabling crowdsourced live event coverage with adaptive collaborative upload strategies[C]∥World of Wireless,Mobile and Multimedia Networks.IEEE,2016:1-3.
[29]WU Y,CAO G.VideoMec:a metadata-enhanced crowdsourcing system for mobile videos[C]∥ACM/IEEE International Conference on Information Processing in Sensor Networks.IEEE,2017:143-154.
[30]DAO T,ROYCHOWDHURY A K,MADHYASTHA H V,et al. Managing Redundant Content in Bandwidth Constrained Wireless Networks[J].IEEE/ACM Transactions on Networking,2017,PP(99):1-16.
[31]WU Y,WANG Y,HU W,et al.Resource-Aware Photo Crow-dsourcing Through Disruption Tolerant Networks[C]∥IEEE,International Conference on Distributed Computing Systems.IEEE,2016:374-383.
[32]WU Y,WANG Y,HU W,et al.Smartphoto:a resource-aware crowdsourcing approach for image sensing with smartphones[J].IEEE Transactions on Mobile Computing,2016,15(5):1249-1263.
[33]WEINSBERG U,LI Q,TAFT N,et al.CARE:content aware redundancy elimination for challenged networks[C]∥Procee-dings of the 11th ACM Workshop on Hot Topics in Networks.ACM,2012:127-132.
[34]MERGEL I.Distributed Democracy:SeeClickFix.Com for Crowdsourced Issue Reporting[J].Ssrn Electronic Journal,2012.
[35]LU Y,COLMENARES J A.Efficient Detection of Points of Interest from Georeferenced Visual Content∥Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data.ACM,2017:27-36.
[36]LI Y,XUE F,FAN X,et al.Pedestrian walking safety system based on smartphone built-in sensors[J].IET Communications,2018,12(6):751-758.
[37]KIM S,ROBSON C,ZIMMERMAN T,et al.Creek watch:pairing usefulness and usability for successful citizen science∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.ACM,2011:2125-2134.
[1] 邵子灏, 杨世宇, 马国杰.
室内信息服务的基础——低成本定位技术研究综述
Foundation of Indoor Information Services:A Survey of Low-cost Localization Techniques
计算机科学, 2022, 49(9): 228-235. https://doi.org/10.11896/jsjkx.210900260
[2] 唐清华, 王玫, 唐超尘, 刘鑫, 梁雯.
基于M2M相遇区的PDR室内定位方法
PDR Indoor Positioning Method Based on M2M Encounter Region
计算机科学, 2022, 49(9): 283-287. https://doi.org/10.11896/jsjkx.210800270
[3] 周楚霖, 陈敬东, 黄凡.
基于无迹粒子滤波的WiFi-PDR融合室内定位技术
WiFi-PDR Fusion Indoor Positioning Technology Based on Unscented Particle Filter
计算机科学, 2022, 49(6A): 606-611. https://doi.org/10.11896/jsjkx.210700108
[4] 李丽, 郑嘉利, 罗文聪, 全艺璇.
基于近端策略优化的RFID室内定位算法
RFID Indoor Positioning Algorithm Based on Proximal Policy Optimization
计算机科学, 2021, 48(4): 274-281. https://doi.org/10.11896/jsjkx.200300028
[5] 徐鹤, 吴满星, 李鹏.
基于ARIMA模型的RFID室内相对位置定位算法
RFID Indoor Relative Position Positioning Algorithm Based on ARIMA Model
计算机科学, 2020, 47(9): 252-257. https://doi.org/10.11896/jsjkx.200400038
[6] 李建军, 汪校铃, 杨玉, 付佳.
基于CQPSO移动群智感知紧急任务分配方法研究
Emergency Task Assignment Method Based on CQPSO Mobile Crowd Sensing
计算机科学, 2020, 47(6A): 273-277. https://doi.org/10.11896/JsJkx.190700040
[7] 李丽,郑嘉利,王哲,袁源,石静.
基于异步优势动作评价的RFID室内定位算法
RFID Indoor Positioning Algorithm Based on Asynchronous Advantage Actor-Critic
计算机科学, 2020, 47(2): 233-238. https://doi.org/10.11896/jsjkx.190100070
[8] 王文博, 黄璞, 杨章静.
基于超宽带、里程计、RGB-D融合的室内定位方法
Indoor Positioning Method Based on UWB Odometer and RGB-D Fusion
计算机科学, 2020, 47(11A): 334-338. https://doi.org/10.11896/jsjkx.200200033
[9] 蔡威, 白光伟, 沈航, 成昭炜, 张慧丽.
移动群智感知中基于强化学习的双赢博弈
Reinforcement Learning Based Win-Win Game for Mobile Crowdsensing
计算机科学, 2020, 47(10): 41-47. https://doi.org/10.11896/jsjkx.200700070
[10] 李卓, 徐哲, 陈昕, 李淑琴.
面向移动群智感知的位置相关在线多任务分配算法
Location-related Online Multi-task Assignment Algorithm for Mobile Crowd Sensing
计算机科学, 2019, 46(6): 102-106. https://doi.org/10.11896/j.issn.1002-137X.2019.06.014
[11] 王哲, 郑嘉利, 李丽, 袁源, 石静.
蝗虫群优化和极限学习机相结合的RFID室内定位算法
RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine
计算机科学, 2019, 46(12): 120-125. https://doi.org/10.11896/jsjkx.181202381
[12] 付先凯, 蒋鑫龙, 刘军发, 张少博, 陈益强.
基于多维尺度分析的自适应室内群终端定位方法
Adaptive Indoor Location Method for Multiple Terminals Based on Multidimensional Scaling
计算机科学, 2018, 45(10): 104-110. https://doi.org/10.11896/j.issn.1002-137X.2018.10.020
[13] 陈诗军, 王慧强, 王园园, 胡海婧.
一种面向室内定位的基站选择优化方法
Base Station Selection Optimization Method Oriented at Indoor Positioning
计算机科学, 2018, 45(10): 115-119. https://doi.org/10.11896/j.issn.1002-137X.2018.10.022
[14] 夏俊, 刘军发, 蒋鑫龙, 陈益强.
针对设备差异性问题的增量式室内定位方法
Incremental Indoor Localization for Device Diversity Issues
计算机科学, 2018, 45(10): 69-77. https://doi.org/10.11896/j.issn.1002-137X.2018.10.014
[15] 宦若虹,陈月.
基于地图信息和位置自适应修正的粒子滤波室内定位方法
Indoor Localization Based on Map Information and Particle Filter with Position Adaptive Correction
计算机科学, 2017, 44(Z11): 297-301. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.063
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!