Name
Title | ||
|---|---|---|
PETRI: Reducing Bandwidth Requirement in Smart Surveillance by Edge-Cloud Collaborative Adaptive Frame Clustering and Pipelined Bidirectional Tracking |
Abstract | ||
|---|---|---|
Neural networks running on cloud servers have been widely used in smart surveillance, but they require high bandwidth to upload videos. Edge-cloud collaborative encoding based on ROI (Region-Of-Interest) can reduce bandwidth requirement, but it suffers from inaccurate ROI detection due to feedback latency and undetected new targets. To address the above challenges, we propose an object detection s... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DAC18074.2021.9586088 | 2021 58th ACM/IEEE Design Automation Conference (DAC) |
Keywords | DocType | ISSN |
Target tracking,Image edge detection,Surveillance,Pipelines,Collaboration,Bandwidth,Object detection | Conference | 0738-100X |
ISBN | Citations | PageRank |
978-1-6654-3274-0 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoyang Liu | 1 | 0 | 0.34 |
| Lu Zhang | 2 | 0 | 0.34 |
| J. Wang | 3 | 479 | 95.23 |
| Huazhong Yang | 4 | 2239 | 214.90 |
| Yongpan Liu | 5 | 1056 | 84.55 |