TY - GEN
T1 - Resolution-Aware Deep Multi-View Camera Systems
AU - Hakimi, Zeinab
AU - Narayanan, Vijaykrishnan
N1 - Publisher Copyright:
© 2021 EDAA.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Recognizing 3D objects with multiple views is an important problem in computer vision. However, multi view object recognition can be challenging for networked embedded intelligent systems (IoT devices) as they have data transmission limitation as well as computational resource constraint. In this work, we design an enhanced multi-view distributed recognition system which deploys a view importance estimator to transmit data with different resolutions. Moreover, a multi-view learning-based super-resolution enhancer is used at the back-end to compensate for the performance degradation caused by information loss from resolution reduction. The extensive experiments on the benchmark dataset demonstrate that the designed resolution-aware multi-view system can decrease the endpoint's communication energy by a factor of 5× while sustaining accuracy. Further experiments on the enhanced multi-view recognition system show that accuracy increment can be achieved with minimum effect on the computational cost of back-end system.
AB - Recognizing 3D objects with multiple views is an important problem in computer vision. However, multi view object recognition can be challenging for networked embedded intelligent systems (IoT devices) as they have data transmission limitation as well as computational resource constraint. In this work, we design an enhanced multi-view distributed recognition system which deploys a view importance estimator to transmit data with different resolutions. Moreover, a multi-view learning-based super-resolution enhancer is used at the back-end to compensate for the performance degradation caused by information loss from resolution reduction. The extensive experiments on the benchmark dataset demonstrate that the designed resolution-aware multi-view system can decrease the endpoint's communication energy by a factor of 5× while sustaining accuracy. Further experiments on the enhanced multi-view recognition system show that accuracy increment can be achieved with minimum effect on the computational cost of back-end system.
UR - http://www.scopus.com/inward/record.url?scp=85111032666&partnerID=8YFLogxK
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U2 - 10.23919/DATE51398.2021.9474182
DO - 10.23919/DATE51398.2021.9474182
M3 - Conference contribution
AN - SCOPUS:85111032666
T3 - Proceedings -Design, Automation and Test in Europe, DATE
SP - 414
EP - 417
BT - Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
Y2 - 1 February 2021 through 5 February 2021
ER -