Name
Abstract | ||
|---|---|---|
As one of the internet of things (IoT) use cases, wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. Videos captured by surveillance cameras are required to be uploaded for further storage and analysis, while the large amount of its raw data brings great challenges to the transmission through resource-constraint wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames before uploading can dramatically relieve the transmission pressure. Additionally, real-world monitoring environment may bring shielding or blind areas in videos, which notoriously affects the accuracy on frame filtering. The collaboration between neighbouring cameras can compensate for such accuracy loss. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.sysarc.2020.101934 | Journal of Systems Architecture |
Keywords | DocType | Volume |
Light-weight AI,IoT collaboration,Wireless surveillance system,Dynamic background modelling,Edge computing | Journal | 114 |
ISSN | Citations | PageRank |
1383-7621 | 0 | 0.34 |
References | Authors | |
27 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutong Liu | 1 | 20 | 5.75 |
| Linghe Kong | 2 | 770 | 72.44 |
| guihai chen | 3 | 3537 | 317.28 |
| Fangqin Xu | 4 | 0 | 0.68 |
| Zhanquan Wang | 5 | 3 | 1.04 |