Demo abstract

On-demand information retrieval from videos using deep learning in wireless networks

Zongqing Lu, Noor Felemban, Kevin Chan, Thomas F. La Porta

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

1 Citation (Scopus)

Abstract

Mobile devices with cameras have greatly assisted in the prevalence of online videos. Valuable information may be retrieved from videos for various purposes. While deep learning enables automatic information retrieval from videos, it is a demanding task for mobile devices despite recent advances in their computational capability. Given a network consisting of mobile devices and a video-cloud, mobile devices may be able to upload videos to the video-cloud, a platform more computationally capable to process videos. However, due to network constraints, once a query initiates a video processing task of a specific interest, most videos will not likely have been uploaded to the video-cloud, especially when the query is about a recent event. We designed and implemented a distributed system for video processing using deep learning across a wireless network, where network devices answer queries by retrieving information from videos stored across the network and reduce query response time by computation o?oad from mobile devices to the video-cloud.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages279-280
Number of pages2
ISBN (Electronic)9781450349666
DOIs
StatePublished - Apr 18 2017
Event2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)

Other

Other2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017
CountryUnited States
CityPittsburgh
Period4/18/174/20/17

Fingerprint

Information retrieval
Mobile devices
Wireless networks
Processing
Cameras
Deep learning

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering
  • Computer Networks and Communications

Cite this

Lu, Z., Felemban, N., Chan, K., & La Porta, T. F. (2017). Demo abstract: On-demand information retrieval from videos using deep learning in wireless networks. In Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week) (pp. 279-280). (Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/3054977.3057296
Lu, Zongqing ; Felemban, Noor ; Chan, Kevin ; La Porta, Thomas F. / Demo abstract : On-demand information retrieval from videos using deep learning in wireless networks. Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week). Association for Computing Machinery, Inc, 2017. pp. 279-280 (Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)).
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abstract = "Mobile devices with cameras have greatly assisted in the prevalence of online videos. Valuable information may be retrieved from videos for various purposes. While deep learning enables automatic information retrieval from videos, it is a demanding task for mobile devices despite recent advances in their computational capability. Given a network consisting of mobile devices and a video-cloud, mobile devices may be able to upload videos to the video-cloud, a platform more computationally capable to process videos. However, due to network constraints, once a query initiates a video processing task of a specific interest, most videos will not likely have been uploaded to the video-cloud, especially when the query is about a recent event. We designed and implemented a distributed system for video processing using deep learning across a wireless network, where network devices answer queries by retrieving information from videos stored across the network and reduce query response time by computation o?oad from mobile devices to the video-cloud.",
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Lu, Z, Felemban, N, Chan, K & La Porta, TF 2017, Demo abstract: On-demand information retrieval from videos using deep learning in wireless networks. in Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week). Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week), Association for Computing Machinery, Inc, pp. 279-280, 2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017, Pittsburgh, United States, 4/18/17. https://doi.org/10.1145/3054977.3057296

Demo abstract : On-demand information retrieval from videos using deep learning in wireless networks. / Lu, Zongqing; Felemban, Noor; Chan, Kevin; La Porta, Thomas F.

Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week). Association for Computing Machinery, Inc, 2017. p. 279-280 (Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)).

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

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Lu Z, Felemban N, Chan K, La Porta TF. Demo abstract: On-demand information retrieval from videos using deep learning in wireless networks. In Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week). Association for Computing Machinery, Inc. 2017. p. 279-280. (Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)). https://doi.org/10.1145/3054977.3057296