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If you see something, swipe towards it: crowdsourced event localization using smartphones

Published: 08 September 2013 Publication History

Abstract

This paper presents iSee, a crowdsourced approach to detecting and localizing events in outdoor environments. Upon spotting an event, an iSee user only needs to swipe on her smartphone's touchscreen in the direction of the event. These swiping directions are often inaccurate and so are the compass measurements. Moreover, the swipes do not encode any notion of how far the event is located from the user, neither is the GPS location of the user accurate. Furthermore, multiple events may occur simultaneously and users do not explicitly indicate which events they are swiping towards. Nonetheless, as more users start contributing data, we show that our proposed system is able to quickly detect and estimate the locations of the events. We have implemented iSee on Android phones and have experimented in real-world settings by planting virtual "events" in our campus and asking volunteers to swipe on seeing one. Results show that iSee performs appreciably better than established triangulation and clustering-based approaches, in terms of localization accuracy, detection coverage, and robustness to sensor noise.

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Cited By

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  • (2024)Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical GuidelinesCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-54531-3_13(229-248)Online publication date: 23-Feb-2024
  • (2023)COSense: collaborative and opportunistic sensing of road events by vehicles’ camerasCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-023-00126-95:3(276-287)Online publication date: 15-Feb-2023
  • (2022)Distant object localization with a single image obtained from a smartphone in an urban environmentInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2022.102820111(102820)Online publication date: Jul-2022
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cover image ACM Conferences
UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
September 2013
846 pages
ISBN:9781450317702
DOI:10.1145/2493432
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 September 2013

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Author Tags

  1. crowdsourcing
  2. event localization
  3. smartphone sensing

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UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;
Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2024)Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical GuidelinesCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-54531-3_13(229-248)Online publication date: 23-Feb-2024
  • (2023)COSense: collaborative and opportunistic sensing of road events by vehicles’ camerasCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-023-00126-95:3(276-287)Online publication date: 15-Feb-2023
  • (2022)Distant object localization with a single image obtained from a smartphone in an urban environmentInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2022.102820111(102820)Online publication date: Jul-2022
  • (2021)Patrolling Automated Guided Vehicle Enhanced with Object and Face Recognition Functions2021 International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB)10.1109/ICEIB53692.2021.9686395(199-203)Online publication date: 10-Dec-2021
  • (2021)A moving track data-based method for gathering behavior prediction at early stageApplied Intelligence10.1007/s10489-021-02244-2Online publication date: 7-Apr-2021
  • (2020)A Neural Network Based Approach for Geo-Localizing Events in Crowd Sourced VideosProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293365(1-9)Online publication date: 3-May-2020
  • (2020)Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-MakingJournal of Management in Engineering10.1061/(ASCE)ME.1943-5479.000074836:3Online publication date: May-2020
  • (2019)A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-MetadataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332170115:3(1-26)Online publication date: 20-Aug-2019
  • (2019)Rulers on Our ArmsACM Transactions on Sensor Networks10.1145/328918315:1(1-25)Online publication date: 5-Feb-2019
  • (2019)CrowdTracking: Real-Time Vehicle Tracking Through Mobile CrowdsensingIEEE Internet of Things Journal10.1109/JIOT.2019.29010936:5(7570-7583)Online publication date: Oct-2019
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