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MARVEL: Enabling Mobile Augmented Reality with Low Energy and Low Latency

Cited 52 time in Web of Science Cited 57 time in Scopus
Authors

Chen, Kaifei; Li, Tong; Kim, Hyung-Sin; Culler, David E.; Katz, Randy H.

Issue Date
2018
Publisher
ASSOC COMPUTING MACHINERY
Citation
SENSYS'18: PROCEEDINGS OF THE 16TH CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS, pp.292-304
Abstract
This paper presents MARVEL, a mobile augmented reality (MAR) system which provides a notation display service with imperceptible latency (< 100 ms) and low energy consumption on regular mobile devices. In contrast to conventional MAR systems, which recognize objects using image-based computations performed in the cloud, MARVEL mainly utilizes a mobile device's local inertial sensors for recognizing and tracking multiple objects, while computing local optical flow and offloading images only when necessary. We propose a system architecture which uses local inertial tracking, local optical flow, and visual tracking in the cloud synergistically. On top of that, we investigate how to minimize the overhead for image computation and offloading. We have implemented and deployed a holistic prototype system in a commercial building and evaluate MARVEL's performance. The efficient use of a mobile device's capabilities lowers latency and energy consumption without sacrificing accuracy.
URI
https://hdl.handle.net/10371/203161
DOI
https://doi.org/10.1145/3274783.3274834
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  • Graduate School of Data Science
Research Area Distributed machine learning, Edge, Mobile AI

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