Name
Abstract | ||
|---|---|---|
In recent years, target detection in maritime surveillance has become a popular subject. Horizon under maritime environment is one of the most important semantic boundaries for segmenting the image into sea and sky. With the help of horizon, seeking scope can be restricted to a definite small area, which significantly reduces the computational complexity. Meanwhile, horizon is used to automatically adjust the attitude of air vehicles as well. Although a plenty of algorithms for horizon detection have been created, they are prone to low visibility and occlusions. In this paper, a novel robust algorithm based on probability distribution and physical characteristics is proposed. At first, the probabilities are distributed to each vertically divided region by weighted textures, and the sea–sky region is located from definite probabilistic intervals. Based on a Canny edge detector, Hough transform extracts a series of candidate horizons. Finally, a novel voting method is applied. The line with the maximum value will be regarded as the true horizon. The proposed algorithm can not only precisely detect the horizon from fine images but also from blurred image, even with splashed camera. The performance of the proposed algorithm is verified by experiments on 65 videos containing more than 20 000 frames under different sceneries. The results illustrate that our algorithm can detect the horizon line with minor errors in comparison with other five state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2019.2893008 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Image color analysis,Feature extraction,Transforms,Image edge detection,Sensors,Computational modeling,Probability distribution | Horizon,Electronic engineering,Electro-optical sensor,Electrical engineering,Mathematics | Journal |
Volume | Issue | ISSN |
69 | 1 | 0018-9456 |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Liang | 1 | 1 | 0.41 |
| Ya Liang | 2 | 1 | 0.41 |