skip to main content
10.1145/3123024.3123094acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

CrowdTracker: object tracking using mobile crowd sensing

Published: 11 September 2017 Publication History

Abstract

This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from traditional video-based studies, CrowdTracker recruits people to collaboratively take photos of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost on user incentives. Specifically, the incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, we propose the MPRE model to predict the object movement and two other algorithms, namely T-centric and P-centric, for task allocation. Initial experimental results over a large-scale real-world dataset indicate that CrowdTracker can effectively track the object with a low incentive cost.

References

[1]
B. Guo, et al. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys, 48(1), 2015.
[2]
L. Liu, et al. Dynamic node collaboration for mobile target tracking in wireless camera sensor networks. In INFOCOM'09, 2009, pp. 1188--1196.
[3]
G. Merlino, et al. Mobile crowdsensing as a service: a platform for applications on top of sensing clouds. Future Generation Computer Systems, 56, 2016, pp. 623--639.
[4]
Andy Y. Xue, et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In ICDE, 2013, pp. 254--265.

Cited By

View all
  • (2022)Extendable Multiple Nodes Recurrent Tracking Framework With RTU++IEEE Transactions on Image Processing10.1109/TIP.2022.319270631(5257-5271)Online publication date: 2022
  • (2020)Duration-Sensitive Task Allocation for Mobile Crowd SensingIEEE Systems Journal10.1109/JSYST.2020.296784714:3(4430-4441)Online publication date: Sep-2020
  • (2020)Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile PhonesIEEE Access10.1109/ACCESS.2020.2979933(1-1)Online publication date: 2020

Index Terms

  1. CrowdTracker: object tracking using mobile crowd sensing

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
    September 2017
    1089 pages
    ISBN:9781450351904
    DOI:10.1145/3123024
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 September 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. mobile crowd sensing
    2. object movement prediction
    3. object tracking
    4. photo taking
    5. task allocation

    Qualifiers

    • Research-article

    Conference

    UbiComp '17

    Acceptance Rates

    Overall Acceptance Rate 764 of 2,912 submissions, 26%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 16 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Extendable Multiple Nodes Recurrent Tracking Framework With RTU++IEEE Transactions on Image Processing10.1109/TIP.2022.319270631(5257-5271)Online publication date: 2022
    • (2020)Duration-Sensitive Task Allocation for Mobile Crowd SensingIEEE Systems Journal10.1109/JSYST.2020.296784714:3(4430-4441)Online publication date: Sep-2020
    • (2020)Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile PhonesIEEE Access10.1109/ACCESS.2020.2979933(1-1)Online publication date: 2020

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media