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Netw."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for \u201cin the wild\u201d mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides\n            <jats:italic>distortion-tolerant image recognition<\/jats:italic>\n            for mobile AR with\n            <jats:italic>imperceptible system latency<\/jats:italic>\n            . CollabAR incorporates both\n            <jats:italic>distortion-tolerant<\/jats:italic>\n            and\n            <jats:italic>collaborative<\/jats:italic>\n            image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for \u201cin the wild\u201d images with severe distortions, while reducing the end-to-end system latency to as low as 18.2\u00a0ms.\n          <\/jats:p>","DOI":"10.1145\/3469033","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T20:05:13Z","timestamp":1633464313000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality"],"prefix":"10.1145","volume":"18","author":[{"given":"Guohao","family":"Lan","sequence":"first","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zida","family":"Liu","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Scargill","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jovan","family":"Stojkovic","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlee","family":"Joe-Wong","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Mountain View, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Gorlatova","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"volume-title":"Proceedings of the ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN\u201920)","author":"Liu Z.","key":"e_1_2_1_1_1","unstructured":"Z. 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