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Sen. Netw."],"published-print":{"date-parts":[[2019,5,31]]},"abstract":"<jats:p>Image-based indoor localization has aroused much interest recently because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at servers. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error.<\/jats:p>\n          <jats:p>\n            To overcome these limitations, we propose a\n            <jats:underline>\n              <jats:bold>h<\/jats:bold>\n            <\/jats:underline>\n            ighly\n            <jats:underline>\n              <jats:bold>a<\/jats:bold>\n            <\/jats:underline>\n            utomated (in terms of image confirmation after taking images)\n            <jats:underline>\n              <jats:bold>i<\/jats:bold>\n            <\/jats:underline>\n            mage-based\n            <jats:underline>\n              <jats:bold>l<\/jats:bold>\n            <\/jats:underline>\n            ocalization algorithm (HAIL), distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only distinguishing visual features for each landmark, and employing the efficient k-d tree to search for features. It further utilizes motion sensors and map constraints to enhance the localization accuracy without user operation. We have implemented HAIL on Android platforms and conducted extensive experiments in a food plaza and a premium shopping mall. Experimental results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.\n          <\/jats:p>","DOI":"10.1145\/3284555","type":"journal-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T22:19:21Z","timestamp":1550873961000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Resource-efficient and Automated Image-based Indoor Localization"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5048-060X","authenticated-orcid":false,"given":"Qun","family":"Niu","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2888-1116","authenticated-orcid":false,"given":"Mingkuan","family":"Li","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1913-6808","authenticated-orcid":false,"given":"Suining","family":"He","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1160-0717","authenticated-orcid":false,"given":"Chengying","family":"Gao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"given":"S. -H.","family":"Gary Chan","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Xiaonan","family":"Luo","sequence":"additional","affiliation":[{"name":"Guilin University of Electrical Technology, Guilin, China"}]}],"member":"320","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"e_1_2_1_1_1","article-title":"Fast and scalable structure-from-motion based localization for high-precision mobile augmented reality systems. mUX","volume":"5","author":"Bae Hyojoon","year":"2016","unstructured":"Hyojoon Bae , Michael Walker , Jules White , Yao Pan , Yu Sun , and Mani Golparvar-Fard . 2016 . 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