{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:32:57Z","timestamp":1751369577475,"version":"3.41.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018AAA0101200"],"award-info":[{"award-number":["2018AAA0101200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61832010, 61632008, 61872081,61632013 and 61972131"],"award-info":[{"award-number":["61832010, 61632008, 61872081,61632013 and 61972131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone\n            <jats:italic>mobile cameras<\/jats:italic>\n            and surveillance\n            <jats:italic>ambient cameras<\/jats:italic>\n            holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general,\n            <jats:italic>ambient-camera-based<\/jats:italic>\n            solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary,\n            <jats:italic>mobile-camera-based<\/jats:italic>\n            solutions are capable of continuously tracking pedestrians; however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing\n            <jats:italic>mobile-camera-based<\/jats:italic>\n            and\n            <jats:italic>ambient-camera-based<\/jats:italic>\n            systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this article, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a\n            <jats:italic>construction of projection map function<\/jats:italic>\n            to automatically calibrate ambient cameras, an\n            <jats:italic>instant crowd fingerprints model<\/jats:italic>\n            to describe user motion patterns, and a\n            <jats:italic>confidence-aware matching<\/jats:italic>\n            algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smartphones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68 m, outperforming the state-of-the-art systems by &gt;75%.\n          <\/jats:p>","DOI":"10.1145\/3446633","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T21:26:00Z","timestamp":1632864360000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Enabling Surveillance Cameras to Navigate"],"prefix":"10.1145","volume":"17","author":[{"given":"Liang","family":"Dong","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Jingao","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Guoxuan","family":"Chi","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Danyang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Xinglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, China"}]},{"given":"Jianbo","family":"Li","sequence":"additional","affiliation":[{"name":"Qingdao University, China"}]},{"given":"Qiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.1392310"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2014.02.001"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214266"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2929257"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809702"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364908090961"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737640"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN49398.2020.9209695"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639134"},{"key":"e_1_2_1_10_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861.","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861. Retrieved from https:\/\/arxiv.org\/abs\/1704.04861. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861. Retrieved from https:\/\/arxiv.org\/abs\/1704.04861."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2017.03.015"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206872"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0152-6"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370280"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.41"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210342"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2307636.2307656"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2933540.2933550"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASS.2014.52"},{"key":"e_1_2_1_20_1","unstructured":"Jerry Ratcliffe. 2006. Video Surveillance of Public Places. Citeseer.  Jerry Ratcliffe. 2006. 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Hauptmann . 2016 . Person re-identification: Past, present and future. arXiv:1610.02984. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:1610.02984. Liang Zheng, Yi Yang, and Alexander G. Hauptmann. 2016. Person re-identification: Past, present and future. arXiv:1610.02984. 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