Ship detection for visual maritime surveillance from non-stationary platforms. (1st September 2017)
- Record Type:
- Journal Article
- Title:
- Ship detection for visual maritime surveillance from non-stationary platforms. (1st September 2017)
- Main Title:
- Ship detection for visual maritime surveillance from non-stationary platforms
- Authors:
- Zhang, Yang
Li, Qing-Zhong
Zang, Feng-Ni - Abstract:
- Abstract: This paper presents a new ship target detection algorithm to achieve efficient visual maritime surveillance from non-stationary surface platforms, e.g., buoys and ships, equipped with CCD cameras. In the proposed detector, the three main steps including horizon detection, background modeling and background subtraction, are all based on Discrete Cosine Transform (DCT). By exploiting the characteristics of DCT blocks, we simply extract the horizon line providing an important cue for sea-surface modeling. The DCT-based feature vectors are calculated as the sample input to a Gaussian mixture model which is effective in representing dynamic ocean textures, such as waves, wakes and foams. Having modeled sea regions, we perform the ship detection using background subtraction followed by foreground segmentation. Experimental results with various maritime images demonstrate that the proposed ship detection algorithm outperforms the traditional techniques in terms of both detection accuracy and real-time performance, especially for complex sea-surface background with large waves. Highlights: DCT-based ship object detection system optimized for non-stationary surface platforms. Effective horizon line detection to segment both sky and sea-surface background regions. New texture-based Gaussian mixture model for sea-surface background clustering. Improved detection accuracy over previous visual attention methods. Real-time performance to meet maritime surveillance videoAbstract: This paper presents a new ship target detection algorithm to achieve efficient visual maritime surveillance from non-stationary surface platforms, e.g., buoys and ships, equipped with CCD cameras. In the proposed detector, the three main steps including horizon detection, background modeling and background subtraction, are all based on Discrete Cosine Transform (DCT). By exploiting the characteristics of DCT blocks, we simply extract the horizon line providing an important cue for sea-surface modeling. The DCT-based feature vectors are calculated as the sample input to a Gaussian mixture model which is effective in representing dynamic ocean textures, such as waves, wakes and foams. Having modeled sea regions, we perform the ship detection using background subtraction followed by foreground segmentation. Experimental results with various maritime images demonstrate that the proposed ship detection algorithm outperforms the traditional techniques in terms of both detection accuracy and real-time performance, especially for complex sea-surface background with large waves. Highlights: DCT-based ship object detection system optimized for non-stationary surface platforms. Effective horizon line detection to segment both sky and sea-surface background regions. New texture-based Gaussian mixture model for sea-surface background clustering. Improved detection accuracy over previous visual attention methods. Real-time performance to meet maritime surveillance video communication. … (more)
- Is Part Of:
- Ocean engineering. Volume 141(2017)
- Journal:
- Ocean engineering
- Issue:
- Volume 141(2017)
- Issue Display:
- Volume 141, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 141
- Issue:
- 2017
- Issue Sort Value:
- 2017-0141-2017-0000
- Page Start:
- 53
- Page End:
- 63
- Publication Date:
- 2017-09-01
- Subjects:
- Ship detection -- Visual maritime surveillance -- Object detection -- Gaussian mixture model -- Discrete cosine transform
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2017.06.022 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 2909.xml