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SakuraSensor: quasi-realtime cherry-lined roads detection through participatory video sensing by cars

Published: 07 September 2015 Publication History

Abstract

In this paper, we propose SakuraSensor, a participatory sensing system which automatically extracts scenic routes information from videos recorded by car-mounted smart-phones and shares the information among users in quasirealtime. As scenic routes information, we target flowering cherries along roads since the best period of flowering cherries is rather short and uncertain from year to year and from place to place. To realize SakuraSensor, we face two technical challenges: (1) how to accurately detect flowering cherries and its degree, and (2) how to efficiently find good places of flowering cherries (PoIs) using the participatory sensing technique. For the first challenge, we develop an image analysis method for detecting image pixels that belong to flowering cherries. To exclude artificial objects with similar color to flowering cherries, we also employ fractal dimension analysis to filter out unnecessary image areas. For the second challenge, we propose a method called k-stage sensing. In this method, the interval for sensing (taking a still image and applying the image analysis) by each car is dynamically shortened so that the roads near the already found PoIs are more densely sensed. We implemented SakuraSensor consisting of client-side software for iOS devices and server-side software for a cloud server and conducted experiments to travel cherry-lined roads and record videos by several cars. As a result, we confirmed that our method can identify flowering cherries at about 74 % precision and 84 % recall. We also confirmed that our k-stage sensing method could achieve the comparable PoI detection rate with half sensing times compared to a conventional method.

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    cover image ACM Conferences
    UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
    September 2015
    1302 pages
    ISBN:9781450335744
    DOI:10.1145/2750858
    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: 07 September 2015

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    Author Tags

    1. k-stage sensing
    2. flowering cherries detection
    3. image analysis
    4. participatory sensing

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    • JSPS KAKENHI

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    • Yahoo! Japan
    • SIGMOBILE
    • FX Palo Alto Laboratory, Inc.
    • ACM
    • Rakuten Institute of Technology
    • Microsoft
    • Bell Labs
    • SIGCHI
    • Panasonic
    • Telefónica
    • ISTC-PC

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    UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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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)Utility-Based Heterogeneous User Recruitment of Multitask in Mobile CrowdsensingIEEE Internet of Things Journal10.1109/JIOT.2023.323667910:11(9796-9808)Online publication date: 1-Jun-2023
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