Name
Abstract | ||
|---|---|---|
The area around plants often encounters with fog weather because of its mountainous location, that results in unclear video surveillance and incorrect observation. While, it is of great significance for power lines to filter the fog and get clear surveillance videos. This paper presents an adaptive real-time video defogging method based on context-sensitiveness by using improved guide filtering algorithm and improving the single-frame image defogging effect within a limited computation time. In order to take full advantage of contextual information, we propose multi-strategy integration video defogging method, experimental results show that the algorithm is able to defog in real time under moving camera videos in premise of ensuring defogging performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/RCAR.2016.7784063 | 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR) |
Keywords | Field | DocType |
adaptive real-time video defogging method,context-sensitiveness,fog weather,mountainous location,video surveillance,power lines,fog filtering,improved guide filtering algorithm,single-frame image defogging effect improvement,multistrategy integration video defogging method,moving camera videos | Computer vision,Contextual information,Computer science,Filter (signal processing),Electric power transmission,Artificial intelligence,Computation | Conference |
ISBN | Citations | PageRank |
978-1-4673-8960-0 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Song | 1 | 113 | 15.51 |
| Bangfei Deng | 2 | 0 | 1.01 |
| Haibing Zhang | 3 | 0 | 0.68 |
| Qianbo Xiao | 4 | 0 | 0.34 |
| Shudi Peng | 5 | 0 | 0.34 |