TY - GEN
T1 - A smart-decision system for realtime mobile AR applications
AU - Huang, Siqi
AU - Han, Tao
AU - Xie, Jiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - With the development of the hardware and software platforms, we can implement the deep learning model on the mobile device for mobile augmented reality (AR) applications. However, not all mobile AR tasks can be finished on mobile devices. Meanwhile, the limited computation resources on mobile devices are still the main obstacle to achieve realtime mobile AR applications. In this paper, we proposed a smart-decision framework which combines the advantages of the on-device mobile AR system and the edge-based mobile AR system to achieve real-time object recognition. High computation complexity tasks will be offloaded to the edge servers. Low complexity tasks will be executed on mobile devices or the edge server depending on the network latency. To overcome the dynamic changes of network condition and the limitations of the on-device deep learning models, we design a cache and matching algorithm on the mobile devices to enhance the performance of the recognition tasks. With our proposed system, the quality of the mobile AR application is improved. The performance of the smart-decision framework is validated through experiments with a testbed.
AB - With the development of the hardware and software platforms, we can implement the deep learning model on the mobile device for mobile augmented reality (AR) applications. However, not all mobile AR tasks can be finished on mobile devices. Meanwhile, the limited computation resources on mobile devices are still the main obstacle to achieve realtime mobile AR applications. In this paper, we proposed a smart-decision framework which combines the advantages of the on-device mobile AR system and the edge-based mobile AR system to achieve real-time object recognition. High computation complexity tasks will be offloaded to the edge servers. Low complexity tasks will be executed on mobile devices or the edge server depending on the network latency. To overcome the dynamic changes of network condition and the limitations of the on-device deep learning models, we design a cache and matching algorithm on the mobile devices to enhance the performance of the recognition tasks. With our proposed system, the quality of the mobile AR application is improved. The performance of the smart-decision framework is validated through experiments with a testbed.
UR - http://www.scopus.com/inward/record.url?scp=85081953037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081953037&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9014186
DO - 10.1109/GLOBECOM38437.2019.9014186
M3 - Conference contribution
AN - SCOPUS:85081953037
T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -