A Computing Platform for Video Crowdprocessing Using Deep Learning

Zongqing Lu, Kevin S. Chan, Thomas F. La Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Mobile devices such as smartphones are enabling users to generate and share videos with increasing rates. In some cases, these videos may contain valuable information, which can be exploited for a variety of purposes. However, instead of centrally collecting and processing videos for information retrieval, we consider crowdprocessing videos, where each mobile device locally processes stored videos. While the computational capability of mobile devices continues to improve, processing videos using deep learning, i.e., convolutional neural networks, is still a demanding task for mobile devices. To this end, we design and build CrowdVision, a computing platform that enables mobile devices to crowdprocess videos using deep learning in a distributed and energy-efficient manner leveraging cloud offload. CrowdVision can quickly and efficiently process videos with offload under various settings and different network connections and greatly outperform the existing computation offload framework (e.g., with a 2× speed-up). In doing so CrowdVision tackles several challenges: (i) how to exploit the characteristics of the computing of deep learning for video processing; (ii) how to parallelize processing and offloading for acceleration; and (iii) how to optimize both time and energy at runtime by just determining the right moments to offload.

Original languageEnglish (US)
Title of host publicationINFOCOM 2018 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1430-1438
Number of pages9
ISBN (Electronic)9781538641286
DOIs
StatePublished - Oct 8 2018
Event2018 IEEE Conference on Computer Communications, INFOCOM 2018 - Honolulu, United States
Duration: Apr 15 2018Apr 19 2018

Publication series

NameProceedings - IEEE INFOCOM
Volume2018-April
ISSN (Print)0743-166X

Other

Other2018 IEEE Conference on Computer Communications, INFOCOM 2018
CountryUnited States
CityHonolulu
Period4/15/184/19/18

Fingerprint

Mobile devices
Processing
Smartphones
Information retrieval
Deep learning
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Lu, Z., Chan, K. S., & La Porta, T. F. (2018). A Computing Platform for Video Crowdprocessing Using Deep Learning. In INFOCOM 2018 - IEEE Conference on Computer Communications (pp. 1430-1438). [8486406] (Proceedings - IEEE INFOCOM; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2018.8486406
Lu, Zongqing ; Chan, Kevin S. ; La Porta, Thomas F. / A Computing Platform for Video Crowdprocessing Using Deep Learning. INFOCOM 2018 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1430-1438 (Proceedings - IEEE INFOCOM).
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Lu, Z, Chan, KS & La Porta, TF 2018, A Computing Platform for Video Crowdprocessing Using Deep Learning. in INFOCOM 2018 - IEEE Conference on Computer Communications., 8486406, Proceedings - IEEE INFOCOM, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 1430-1438, 2018 IEEE Conference on Computer Communications, INFOCOM 2018, Honolulu, United States, 4/15/18. https://doi.org/10.1109/INFOCOM.2018.8486406

A Computing Platform for Video Crowdprocessing Using Deep Learning. / Lu, Zongqing; Chan, Kevin S.; La Porta, Thomas F.

INFOCOM 2018 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1430-1438 8486406 (Proceedings - IEEE INFOCOM; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lu Z, Chan KS, La Porta TF. A Computing Platform for Video Crowdprocessing Using Deep Learning. In INFOCOM 2018 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1430-1438. 8486406. (Proceedings - IEEE INFOCOM). https://doi.org/10.1109/INFOCOM.2018.8486406