Abstract
We consider video processing to detect complex activities in a distributed network consisting of mobile devices and video-cloud servers. To address varying task requirements and resource-constraints of mobile devices, we consider fragmentation of the video processing workflow. Fragmentation of the workflow allows for the mobile device to filter video clips based on metadata, process portions of the clips, and offload other videos to video-cloud servers. In certain situations, the cloud may require access to the raw video even if the video is processed by a mobile device. Through the use of resizing the image and a top-k analysis, we explore various quality of information metrics in the addressing of complex activity detection that consider energy usage of the mobile devices and the quality of detecting objects in terms of completeness and timeliness.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1345-1348 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509045457 |
| DOIs | |
| State | Published - Apr 19 2017 |
| Event | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016 |
Publication series
| Name | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
|---|
Other
| Other | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 12/7/16 → 12/9/16 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Networks and Communications
Cite this
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Video processing of complex activity detection in resource-constrained networks. / Felemban, Noor; Lu, Zongqing; Porta, Thomas La; Chan, Kevin.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1345-1348 7906060 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Video processing of complex activity detection in resource-constrained networks
AU - Felemban, Noor
AU - Lu, Zongqing
AU - Porta, Thomas La
AU - Chan, Kevin
PY - 2017/4/19
Y1 - 2017/4/19
N2 - We consider video processing to detect complex activities in a distributed network consisting of mobile devices and video-cloud servers. To address varying task requirements and resource-constraints of mobile devices, we consider fragmentation of the video processing workflow. Fragmentation of the workflow allows for the mobile device to filter video clips based on metadata, process portions of the clips, and offload other videos to video-cloud servers. In certain situations, the cloud may require access to the raw video even if the video is processed by a mobile device. Through the use of resizing the image and a top-k analysis, we explore various quality of information metrics in the addressing of complex activity detection that consider energy usage of the mobile devices and the quality of detecting objects in terms of completeness and timeliness.
AB - We consider video processing to detect complex activities in a distributed network consisting of mobile devices and video-cloud servers. To address varying task requirements and resource-constraints of mobile devices, we consider fragmentation of the video processing workflow. Fragmentation of the workflow allows for the mobile device to filter video clips based on metadata, process portions of the clips, and offload other videos to video-cloud servers. In certain situations, the cloud may require access to the raw video even if the video is processed by a mobile device. Through the use of resizing the image and a top-k analysis, we explore various quality of information metrics in the addressing of complex activity detection that consider energy usage of the mobile devices and the quality of detecting objects in terms of completeness and timeliness.
UR - http://www.scopus.com/inward/record.url?scp=85019034328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019034328&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2016.7906060
DO - 10.1109/GlobalSIP.2016.7906060
M3 - Conference contribution
AN - SCOPUS:85019034328
T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
SP - 1345
EP - 1348
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -