skip to main content
10.1145/3004010.3004050acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobiquitousConference Proceedingsconference-collections
research-article

A Real-Time Detection and Localization System of Unexpected Events Using Crowd-Sourcing

Published:28 November 2016Publication History

ABSTRACT

Through crowd sourcing, we have been developing a realtime detection and localization system of unexpected events such as traffic accidents. For such a study, we previously proposed a crowd-sourced system called Smart and Quick Unexpected-Event Detect (SQUED) system that used smartphone sensor data for the detection and localization of unexpected events. When SQUED system users encounter an event, they point their smartphones toward the direction of the event. SQUED system can detect the location and time of the event using smartphone sensor data. In this study, we further developed our SQUED system to realize real-time detection and localization function. In addition, we developed the pseudo-user generator to evaluate our systems. Our pseudo-user generator can simulate the environment in which the number of users has increased. As a result, it was confirmed that our real-time system in combination with the pseudo-user generator could perform event detection 100 times faster than our previous system.

References

  1. A. Harma, M.F. McKinney, and J. Skowronek. Automatic surveillance of the acoustic activity in our living environment. In IEEE ICME 2005, pp. 4 pp. 6--8, July 2005.Google ScholarGoogle ScholarCross RefCross Ref
  2. Alan F. Smeaton and Mike McHugh. Towards event detection in an audio-based sensor network. In ACM, VSSN '05, pp. 87--94, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Robin Wentao Ouyang, et al. If you see something, swipe towards it: Crowdsourced event localization using smartphones. In ACM, UbiComp '13, pp. 23--32, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. T. Yamamoto, K. Oku, Hung-Hsuan Huang, and K. Kawagoe. Squed: A novel crowd-sourced system for detection and localization of unexpected events from smartphone-sensor data. In IEEE/ACIS ICIS 2015, pp. 383--386, June 2015.Google ScholarGoogle ScholarCross RefCross Ref
  5. Du Tran, Junsong Yuan, and D. Forsyth. Video event detection: From subvolume localization to spatiotemporal path search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 36, No. 2, pp. 404--416, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Feng Wang, Zhanhu Sun, Yu-Gang Jiang, and Chong-Wah Ngo. Video event detection using motion relativity and feature selection. Multimedia, IEEE Transactions on, Vol. 16, No. 5, pp. 1303--1315, 2014.Google ScholarGoogle Scholar
  7. Tong Qin, Huadong Ma, Dong Zhao, Tianyuan Li, and Jianwei Chen. Crowdsourcing based event reporting system using smartphones with accurate localization and photo tamper detection. In Big Data Computing and Communications, Vol. 9196 of Lecture Notes in Computer Science, pp. 141--151. 2015.Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    MOBIQUITOUS 2016: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services
    November 2016
    280 pages
    ISBN:9781450347594
    DOI:10.1145/3004010

    Copyright © 2016 ACM

    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 November 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate26of87submissions,30%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader