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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week) |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 279-280 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450349666 |
| DOIs | |
| State | Published - Apr 18 2017 |
| Event | 2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 - Pittsburgh, United States Duration: Apr 18 2017 → Apr 20 2017 |
Publication series
| Name | Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week) |
|---|
Other
| Other | 2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 |
|---|---|
| Country | United States |
| City | Pittsburgh |
| Period | 4/18/17 → 4/20/17 |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Control and Systems Engineering
- Computer Networks and Communications
Cite this
}
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 proceeding › Conference contribution
TY - GEN
T1 - Demo abstract
T2 - On-demand information retrieval from videos using deep learning in wireless networks
AU - Lu, Zongqing
AU - Felemban, Noor
AU - Chan, Kevin
AU - La Porta, Thomas F.
PY - 2017/4/18
Y1 - 2017/4/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85019012733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019012733&partnerID=8YFLogxK
U2 - 10.1145/3054977.3057296
DO - 10.1145/3054977.3057296
M3 - Conference contribution
AN - SCOPUS:85019012733
T3 - Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)
SP - 279
EP - 280
BT - Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)
PB - Association for Computing Machinery, Inc
ER -